随机布设多天线信号合成关键技术研究
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摘要
如何保障微弱信号的高效可靠接收一直是现代通信中的一个难点。目前由于技术水平的限制，天线口径不可能无限制提高，接收机噪声也很难进一步降低，一种有效的解决方法是利用多个天线对同一信号进行联合接收，将接收信号进行合成以提高信号质量。通过多天线信号的有效合成，增大了天线的等效口径，从而能够在较低的信噪比条件下实现高速可靠的数据通信。随机布设多天线信号合成具有应用灵活、成本较低以及性能稳健等优点，在众多领域具有广泛应用前景。目前该技术的研究工作主要针对深空探测这一特殊应用领域开展。在更为一般的场景中，利用多个天线所接收的信号进行合成，需要解决无先验信息条件下的信号间时延差异估计、频率相位差异估计及合成权值估计等问题。解决这些问题是该技术获得进一步推广应用的关键所在。
本文主要围绕随机布设多天线信号波形合成中的关键技术开展研究，针对多天线信号的最佳合成权值估计、时延差异估计补偿、频差估计补偿以及多径信道下的合成问题进行讨论分析。论文的主要创新点可以概括为以下几个方面：
1、针对基于最大输出信噪比和最大输出功率准则下的经典合成权值估计方法中存在的噪声方差难以准确估计、估计量有偏等问题，本文首先从理论上证明了以自相关系数为目标函数与以信噪比为目标函数在进行信号合成权值估计中的等价关系，基于上述等价关系，分别提出了针对两路信号和多路信号合成的最佳权值估计算法。传统的以合成信号信噪比作为目标函数的算法不能用于噪声方差难以准确估计的场合。而当各路信号的噪声方差不相等时，以合成信号功率为目标函数的算法为有偏估计。为了解决这些问题，本文通过理论推导证明了合成信号的自相关系数作为信号合成目标函数的正确性。根据该目标函数，提出了一种针对两路信号合成的ACE算法。针对多路信号合成权值估计问题，本文用特征值分解的方法提出了AC EIGEN算法。并利用一组信号相关矩阵的线性组合代替AC EIGEN算法中的信号相关矩阵，给出了MAC EIGEN算法，进一步提升了低信噪比条件下的合成性能。上述算法无需定时同步，与信号调制方式、信号带宽无关，无需估计噪声方差，适用于噪声方差不等、存在相关噪声的情景，具有更好的通用性。仿真结果表明，AC EIGEN算法和MAC EIGEN算法在低信噪比条件下的合成性能优于传统方法。
2、证明了以归一化峰度为目标函数和以信噪比为目标函数进行最佳合成权值估计的等价关系，提出了一种以归一化峰度为目标函数的合成权值迭代搜索算法。仿真结果表明，通过迭代可以获得比ACE算法更低的方差性能。通过理论推导发现以合成信号的归一化峰度、信号瞬时功率归一化均方差等统计量在作为合成权值估计的目标函数，与以合成信号信噪比为目标函数是等价的。根据该等价关系，基于信号分量和噪声分量统计特性不同的假设，提出了以合成信号归一化峰度为目标函数的合成权值迭代搜索算法。该迭代搜索算法无需估计噪声方差，对信号调制参数透明，也无需定时同步。通过仿真发现，该算法可用于相关噪声条件下合成权值估计，并且通过迭代可以获得比ACE算法更低的方差性能。
3、针对传统的自适应时延估计算法在多天线信号时延对齐中性能较差的问题，提出了以合成信号为参考的多信号时延自适应联合估计算法。从理论上证明了该算法的收敛性和估计量的渐进无偏性，并给出了算法估计量方差性能的理论分析结果。理论分析结果与仿真表明，与基于单路信号为参考的传统自适应时延估计算法相比，该算法降低了估计的方差，提高了多天线信号自适应时延对准性能。传统的自适应信号时延差异估计算法只针对两路信号之间的时差进行估计和补偿，而在例如多天线信号合成、目标定位等应用中，往往需要估计多路信号的时延差异。本文提出的自适应时延联合估计算法，以合成输出信号为公共的参考信号，各路信号分别根据与参考信号的误差自适应调整滤波器参数，最终通过迭代实现时延差异的估计与信号的对齐合成。本文对算法的收敛性给予了证明，并且理论推导了算法的均值特性和方差特性。仿真结果证明了理论推导的正确性。
4、将对多天线信号之间频差问题转化为线性时变相位问题，本文首先提出了一种自适应相位差异估计补偿算法，证明了算法的收敛性和渐进无偏性。将该算法推广应用于信号间频差估计与补偿，提出了一种方差性能接近CRLB的无偏估计算法和一种方差性能良好且偏差可控的有偏估计算法。针对多天线信号之间的线性时变相位差异，按照相位差信号维纳解的形式，提出了渐进无偏的约束自适应相位差异估计补偿算法。基于该算法的相位差估计结果，本文分别利用时间平均的方法和线性拟合的方法提出了两种频差估计算法。理论推导和仿真结果显示基于时间平均的频差估计算法为有偏估计。同时仿真结果还显示，基于时间平均的频差估计算法的方差低于无偏估计的CRLB，而基于线性拟合的频差估计算法是一种性能接近CRLB的无偏估计算法。
5、针对在无精确信道估计条件下，时域波形合成很难使得信号带宽范围内所有频点的信噪比最大化的问题，根据多径衰落信道下最佳合成架构，提出了基于余弦调制滤波器组的子带合成算法。仿真结果显示，该算法合成输出信号的解调质量优于最大比合并分集信号。根据宽带衰落信号窄带化处理的思想，提出了一种子带合成算法。该算法采用余弦调制分析综合滤波器组将接收信号分为若干带宽很小的子带信号，并且分别将各路对应的子带信号进行最大比合并。该算法对信号调制方式透明，无需进行定时同步，也无需估计信道响应。仿真显示该子带合成算法性能优于最大比合并的分集算法。
How to efficiently and reliably receive the weak signal is one of major problems andchallenges for communication technology. Due to the limitations of the technical level, it's verydifficult to further increase the antenna aperture or reduce the receiver noise. An effectivesolution is to use multiple antennas to joint receive the source signal. Received signals arecombined in order to improve the signal quality. The multi-antenna signal may be combined toimprove the effective aperture of the antenna. This is an effective method to solve thecontradiction between the low signal-to-noise ratio and high-speed communications needs.Multiple signals combining technology of random distribution antennas has the advantages offlexible, low cost and robust performance. It can be applied in many fields. Currently, thetechnology is mainly researched and used in the special environment of deep space exploration.Promotion and application of the technology in a more general application environment is facingmany difficulties such as irregular distribution of antennas, the individual differences of thereceiver and fading channel problems. To solve the problems under more general applicationenvironments is the key to further promote the application of the technology that must beaddressed.
The researching works of this paper around the key technologies of signal waveformcombining on randomly distribution antennas. The time delay estimation and compensation, thefrequency difference estimation and compensation, the optimal combining weight estimation andcombining technology of multipath fading channel are discussed and analyzed in this paper. Themain work and innovations of the thesis can be summarized as following aspects.
1. The classical combining algorithms under the criteria of maximum outputsignal-to-noise ratio and the maximum output power have the problems of estimating noisevariance and the biased ness of estimation. This paper presents and proves a new objectfunction, the combined signal autocorrelation coefficient, which is equivalent to thesignal-to-noise ratio in combining. The criterion of maximum combined signalautocorrelation coefficient is proposed for optimal signal combining. This paper alsoproposed combining weight estimation algorithms for two signals and multiple signalsrespectively according to the new criterion. For conventional combining weight estimation,algorithms using the combined signal SNR as the object function fail in some applicationscenarios which are very different to estimate the noise variance. The estimating algorithmsusing the combined signal power as the object function is biased with not uniform noisevariances. To solve these problems, this paper proposed and proved that the combined signalautocorrelation coefficient can be used as the object function in signal combining, which is equivalent to the combined signal SNR. Using this object function, the paper proposed a simplealgorithm for2signals combining. Using the eigenvalue decomposition method, the paperproposed the AC EIGEN algorithm for multi-signal combining weight estimation. Using a linearcombination of a set of signal correlation matrices instead the correlation matrix, the MACEIGEN algorithm is proposed. And the combining performance under low SNR environment isfurther enhanced. These algorithms do not need timing synchronization, has nothing to do withthe signal modulation, the signal bandwidth, no need to estimate the noise variance of signal, canalso be applied to scenarios of non-uniform noise power and related noise. Algorithms are withexcellent performance and high universality. Simulation results show that proposed algorithmsoutperform classical algorithms under low SNR environment.
2. The equivalence between combined signal normalized kurtosis and SNR is proved.Using the new criterion, an iterative algorithm is proposed. The simulation results showthat iteration can get a lower variance than ACE algorithm. This paper found that statisticssuch as the combined signal normalized kurtosis, normalized mean square error of signalinstantaneous power are equivalent to the combined signal SNR in signal combining as theobject function. According to the difference of statistical properties of signal and noise, aniterative algorithm is proposed based on it. These algorithms do not need timing synchronization,has nothing to do with the signal modulation, the signal bandwidth, no need to estimate the noisevariance of signal, can also be applied to scenarios of non-uniform noise power and related noise.Simulation results show that proposed algorithms outperform the ACE algorithms with iteration.
3. The paper proposed a joint adaptive estimation and compensation algorithm basedon signal combining for poor performance of traditional adaptive time delay estimationalgorithm in multi-antenna signal delay alignment. The paper theoretically proves theconvergence of the algorithm and the progressive unbiasedness of the estimation. Thepaper gives the theoretical analysis of variance performance. Theoretical analysis andsimulation results show that the new algorithm effectively reduces the variance of theadaptive time delay estimation, and it improves the alignment performance ofmulti-antenna signals compared with the conventional adaptive algorithm. The traditionaladaptive time delay estimation algorithms estimate and compensate difference of time delaybetween two signals. There are greater than or equal to3signal delay differences needs to beestimated in applications such as multi-antenna signal combining, targeting locating. Theproposed algorithm uses the combined signal as the common reference signal for joint adaptivetime delay estimate. The algorithm adaptive adjusts the filtering parameters of each signalrespectively based on the error with the reference signal. Ultimately, the algorithm achieves theestimate of time delays and the alignment of all signals through the iterative. Performance derivation and numerical simulation results show that the algorithm is effective to reduce thetime delay estimation variance, and to improve signal alignment performance.
4. Frequency offset between the multi-antenna signals is transformed into lineartime-varying phase shift. This paper derives an asymptotic unbiased constraint adaptivephase shift estimation and compensation algorithm. The algorithm is extended to thefrequency difference estimation and compensation. Two frequency difference estimationalgorithms with variance performance close to the CRLB are proposed. According to theWiener solution in the form of phase shift signals, the paper proposes an asymptotic unbiasedconstraint adaptive phase shift estimation and compensation algorithm. Based on estimationresults of the phase shift estimation, this paper proposed two frequency difference estimationalgorithms using the time-averaged method and linear fitting method. The simulation resultsshow that the frequency difference estimation algorithm based on the average method is anasymptotic unbiased algorithm, with the variance lower than the CRLB. And the algorithm basedon the linear fit method is an unbiased algorithm with the variance close to the CRLB.
5. Maximum ratio combining diversity algorithm in multipath fading channels can notmaximize the full-band signal-to-noise ratio of the combined signal. According to theoptimal combining theory of multipath fading signals, this paper proposed a sub-bandcombining algorithm using the classical cosine-modulated filter banks. Simulation resultsindicate that the demodulation performance of sub-band combined signal outperforms themaximum ratio combining diversity signal. According to the optimal combining model of thefading channel, the cosine modulated filter banks were introduced into the signal combining.And a sub-band combining algorithm is proposed. The classical cosine modulated analysis filterbanks are used to narrow band the received signals. And the corresponding sub-band signals arecombined with the criterion of maximum ratio combining. The algorithm is transparent to thesignal modulation method, without timing synchronization, makes the combined signal tomaximize the signal-to-noise ratio, and improves the quality of signal demodulation. Simulationresults indicate that the demodulation performance of sub-band combined algorithm outperformsthe maximum ratio combining diversity algorithm.
本文主要围绕随机布设多天线信号波形合成中的关键技术开展研究，针对多天线信号的最佳合成权值估计、时延差异估计补偿、频差估计补偿以及多径信道下的合成问题进行讨论分析。论文的主要创新点可以概括为以下几个方面：
1、针对基于最大输出信噪比和最大输出功率准则下的经典合成权值估计方法中存在的噪声方差难以准确估计、估计量有偏等问题，本文首先从理论上证明了以自相关系数为目标函数与以信噪比为目标函数在进行信号合成权值估计中的等价关系，基于上述等价关系，分别提出了针对两路信号和多路信号合成的最佳权值估计算法。传统的以合成信号信噪比作为目标函数的算法不能用于噪声方差难以准确估计的场合。而当各路信号的噪声方差不相等时，以合成信号功率为目标函数的算法为有偏估计。为了解决这些问题，本文通过理论推导证明了合成信号的自相关系数作为信号合成目标函数的正确性。根据该目标函数，提出了一种针对两路信号合成的ACE算法。针对多路信号合成权值估计问题，本文用特征值分解的方法提出了AC EIGEN算法。并利用一组信号相关矩阵的线性组合代替AC EIGEN算法中的信号相关矩阵，给出了MAC EIGEN算法，进一步提升了低信噪比条件下的合成性能。上述算法无需定时同步，与信号调制方式、信号带宽无关，无需估计噪声方差，适用于噪声方差不等、存在相关噪声的情景，具有更好的通用性。仿真结果表明，AC EIGEN算法和MAC EIGEN算法在低信噪比条件下的合成性能优于传统方法。
2、证明了以归一化峰度为目标函数和以信噪比为目标函数进行最佳合成权值估计的等价关系，提出了一种以归一化峰度为目标函数的合成权值迭代搜索算法。仿真结果表明，通过迭代可以获得比ACE算法更低的方差性能。通过理论推导发现以合成信号的归一化峰度、信号瞬时功率归一化均方差等统计量在作为合成权值估计的目标函数，与以合成信号信噪比为目标函数是等价的。根据该等价关系，基于信号分量和噪声分量统计特性不同的假设，提出了以合成信号归一化峰度为目标函数的合成权值迭代搜索算法。该迭代搜索算法无需估计噪声方差，对信号调制参数透明，也无需定时同步。通过仿真发现，该算法可用于相关噪声条件下合成权值估计，并且通过迭代可以获得比ACE算法更低的方差性能。
3、针对传统的自适应时延估计算法在多天线信号时延对齐中性能较差的问题，提出了以合成信号为参考的多信号时延自适应联合估计算法。从理论上证明了该算法的收敛性和估计量的渐进无偏性，并给出了算法估计量方差性能的理论分析结果。理论分析结果与仿真表明，与基于单路信号为参考的传统自适应时延估计算法相比，该算法降低了估计的方差，提高了多天线信号自适应时延对准性能。传统的自适应信号时延差异估计算法只针对两路信号之间的时差进行估计和补偿，而在例如多天线信号合成、目标定位等应用中，往往需要估计多路信号的时延差异。本文提出的自适应时延联合估计算法，以合成输出信号为公共的参考信号，各路信号分别根据与参考信号的误差自适应调整滤波器参数，最终通过迭代实现时延差异的估计与信号的对齐合成。本文对算法的收敛性给予了证明，并且理论推导了算法的均值特性和方差特性。仿真结果证明了理论推导的正确性。
4、将对多天线信号之间频差问题转化为线性时变相位问题，本文首先提出了一种自适应相位差异估计补偿算法，证明了算法的收敛性和渐进无偏性。将该算法推广应用于信号间频差估计与补偿，提出了一种方差性能接近CRLB的无偏估计算法和一种方差性能良好且偏差可控的有偏估计算法。针对多天线信号之间的线性时变相位差异，按照相位差信号维纳解的形式，提出了渐进无偏的约束自适应相位差异估计补偿算法。基于该算法的相位差估计结果，本文分别利用时间平均的方法和线性拟合的方法提出了两种频差估计算法。理论推导和仿真结果显示基于时间平均的频差估计算法为有偏估计。同时仿真结果还显示，基于时间平均的频差估计算法的方差低于无偏估计的CRLB，而基于线性拟合的频差估计算法是一种性能接近CRLB的无偏估计算法。
5、针对在无精确信道估计条件下，时域波形合成很难使得信号带宽范围内所有频点的信噪比最大化的问题，根据多径衰落信道下最佳合成架构，提出了基于余弦调制滤波器组的子带合成算法。仿真结果显示，该算法合成输出信号的解调质量优于最大比合并分集信号。根据宽带衰落信号窄带化处理的思想，提出了一种子带合成算法。该算法采用余弦调制分析综合滤波器组将接收信号分为若干带宽很小的子带信号，并且分别将各路对应的子带信号进行最大比合并。该算法对信号调制方式透明，无需进行定时同步，也无需估计信道响应。仿真显示该子带合成算法性能优于最大比合并的分集算法。
How to efficiently and reliably receive the weak signal is one of major problems andchallenges for communication technology. Due to the limitations of the technical level, it's verydifficult to further increase the antenna aperture or reduce the receiver noise. An effectivesolution is to use multiple antennas to joint receive the source signal. Received signals arecombined in order to improve the signal quality. The multi-antenna signal may be combined toimprove the effective aperture of the antenna. This is an effective method to solve thecontradiction between the low signal-to-noise ratio and high-speed communications needs.Multiple signals combining technology of random distribution antennas has the advantages offlexible, low cost and robust performance. It can be applied in many fields. Currently, thetechnology is mainly researched and used in the special environment of deep space exploration.Promotion and application of the technology in a more general application environment is facingmany difficulties such as irregular distribution of antennas, the individual differences of thereceiver and fading channel problems. To solve the problems under more general applicationenvironments is the key to further promote the application of the technology that must beaddressed.
The researching works of this paper around the key technologies of signal waveformcombining on randomly distribution antennas. The time delay estimation and compensation, thefrequency difference estimation and compensation, the optimal combining weight estimation andcombining technology of multipath fading channel are discussed and analyzed in this paper. Themain work and innovations of the thesis can be summarized as following aspects.
1. The classical combining algorithms under the criteria of maximum outputsignal-to-noise ratio and the maximum output power have the problems of estimating noisevariance and the biased ness of estimation. This paper presents and proves a new objectfunction, the combined signal autocorrelation coefficient, which is equivalent to thesignal-to-noise ratio in combining. The criterion of maximum combined signalautocorrelation coefficient is proposed for optimal signal combining. This paper alsoproposed combining weight estimation algorithms for two signals and multiple signalsrespectively according to the new criterion. For conventional combining weight estimation,algorithms using the combined signal SNR as the object function fail in some applicationscenarios which are very different to estimate the noise variance. The estimating algorithmsusing the combined signal power as the object function is biased with not uniform noisevariances. To solve these problems, this paper proposed and proved that the combined signalautocorrelation coefficient can be used as the object function in signal combining, which is equivalent to the combined signal SNR. Using this object function, the paper proposed a simplealgorithm for2signals combining. Using the eigenvalue decomposition method, the paperproposed the AC EIGEN algorithm for multi-signal combining weight estimation. Using a linearcombination of a set of signal correlation matrices instead the correlation matrix, the MACEIGEN algorithm is proposed. And the combining performance under low SNR environment isfurther enhanced. These algorithms do not need timing synchronization, has nothing to do withthe signal modulation, the signal bandwidth, no need to estimate the noise variance of signal, canalso be applied to scenarios of non-uniform noise power and related noise. Algorithms are withexcellent performance and high universality. Simulation results show that proposed algorithmsoutperform classical algorithms under low SNR environment.
2. The equivalence between combined signal normalized kurtosis and SNR is proved.Using the new criterion, an iterative algorithm is proposed. The simulation results showthat iteration can get a lower variance than ACE algorithm. This paper found that statisticssuch as the combined signal normalized kurtosis, normalized mean square error of signalinstantaneous power are equivalent to the combined signal SNR in signal combining as theobject function. According to the difference of statistical properties of signal and noise, aniterative algorithm is proposed based on it. These algorithms do not need timing synchronization,has nothing to do with the signal modulation, the signal bandwidth, no need to estimate the noisevariance of signal, can also be applied to scenarios of non-uniform noise power and related noise.Simulation results show that proposed algorithms outperform the ACE algorithms with iteration.
3. The paper proposed a joint adaptive estimation and compensation algorithm basedon signal combining for poor performance of traditional adaptive time delay estimationalgorithm in multi-antenna signal delay alignment. The paper theoretically proves theconvergence of the algorithm and the progressive unbiasedness of the estimation. Thepaper gives the theoretical analysis of variance performance. Theoretical analysis andsimulation results show that the new algorithm effectively reduces the variance of theadaptive time delay estimation, and it improves the alignment performance ofmulti-antenna signals compared with the conventional adaptive algorithm. The traditionaladaptive time delay estimation algorithms estimate and compensate difference of time delaybetween two signals. There are greater than or equal to3signal delay differences needs to beestimated in applications such as multi-antenna signal combining, targeting locating. Theproposed algorithm uses the combined signal as the common reference signal for joint adaptivetime delay estimate. The algorithm adaptive adjusts the filtering parameters of each signalrespectively based on the error with the reference signal. Ultimately, the algorithm achieves theestimate of time delays and the alignment of all signals through the iterative. Performance derivation and numerical simulation results show that the algorithm is effective to reduce thetime delay estimation variance, and to improve signal alignment performance.
4. Frequency offset between the multi-antenna signals is transformed into lineartime-varying phase shift. This paper derives an asymptotic unbiased constraint adaptivephase shift estimation and compensation algorithm. The algorithm is extended to thefrequency difference estimation and compensation. Two frequency difference estimationalgorithms with variance performance close to the CRLB are proposed. According to theWiener solution in the form of phase shift signals, the paper proposes an asymptotic unbiasedconstraint adaptive phase shift estimation and compensation algorithm. Based on estimationresults of the phase shift estimation, this paper proposed two frequency difference estimationalgorithms using the time-averaged method and linear fitting method. The simulation resultsshow that the frequency difference estimation algorithm based on the average method is anasymptotic unbiased algorithm, with the variance lower than the CRLB. And the algorithm basedon the linear fit method is an unbiased algorithm with the variance close to the CRLB.
5. Maximum ratio combining diversity algorithm in multipath fading channels can notmaximize the full-band signal-to-noise ratio of the combined signal. According to theoptimal combining theory of multipath fading signals, this paper proposed a sub-bandcombining algorithm using the classical cosine-modulated filter banks. Simulation resultsindicate that the demodulation performance of sub-band combined signal outperforms themaximum ratio combining diversity signal. According to the optimal combining model of thefading channel, the cosine modulated filter banks were introduced into the signal combining.And a sub-band combining algorithm is proposed. The classical cosine modulated analysis filterbanks are used to narrow band the received signals. And the corresponding sub-band signals arecombined with the criterion of maximum ratio combining. The algorithm is transparent to thesignal modulation method, without timing synchronization, makes the combined signal tomaximize the signal-to-noise ratio, and improves the quality of signal demodulation. Simulationresults indicate that the demodulation performance of sub-band combined algorithm outperformsthe maximum ratio combining diversity algorithm.
引文
[1] D. H. Rogstad, A. Mileant, and T. T. Pham, Antenna Arraying Techniques in the Deep SpaceNetwork [M], Hoboken, New Jersey: John Wiley2003.
[2] Potter P, Merrick W, Ludwig A. Large Antenna Apertures and Arrays for Deep SpaceCommunications[R]. JPL Technical Report No.32-848. November1965.
[3] Urech J. Telemetry Improvement Proposal for the85-feet Antenna Network[R]. Space ProgramsSummary37-63,11, JPL, May31,1970.
[4]姚飞，匡麟玲，詹亚锋，陆建华.深空通信天线组阵关键技术及其发展趋势[J].宇航学报.2010，10(31)：2231-2238.
[5]于志坚.深空测控通信系统[M].北京：国防工业出版社，2009:1-11，95-123，291-320
[6] Mileant A, Hinedi S. Overview of arraying techniques for deep space communications[J].IEEE Trans on Comm.,1994,42(2):1856-1865.
[7] Durgadas S. Bagri, Joseph I. Statman, and Mark S. Gatti， Proposed Array-Based Deep SpaceNetwork for NASA，Proceedings of the IEEE，Vol.95, No.10, October2007:1916-1922.
[8]王慧君.天线组阵技术及其在深空网中的应用[C].信息产业部雷达专业第十六届年会论文集，2005,11:269-276.
[9]张乃通，李晖，张钦宇.深空探测通信技术发展趋势及思考[J].宇航学报.2007,7(28):786-793.
[10]叶培建，彭兢.深空探测与我国深空探测展望[J].中国工程科学.2006,10(8):13-18.
[11]李小梅，周三文，李海涛，卢满宏.天线组阵技术验证试验与数据分析[C].第十五届全国遥测遥控技术年会论文集.2008,11:339-344.
[12]徐瑞，李海涛，尚海滨等.利用罗塞塔探测器进行天线组阵试验可行性分析[C].中国宇航学会深空探测技术专业委员会第五届年会论文集.2008,10:53-59.
[13]欧阳自远.2013，中国有望飞往火星—解读中国自主火星探测的路径与难点.2010.10.26.http://pdf.news365.com.cn/whpdf/20101026/WH10102612.pdf.
[14]王骞,于宏毅.深空网中的天线组阵技术及其发展[J].信息工程大学学报.2009,3:365-368.
[15]沈彩耀.多天线信号联合接收的合成技术研究[D]郑州:解放军信息工程大学,2011.
[16]S.J.Dolinar and S.Kalson. Maximum Likelihood Estimation of Signal to Noise Ratio andCombiner Weight [R]. TDA Progress Report42-85, Jet Propulsion Laboratory PasadenaCalifornia, pp.32-41. January-March1986.
[17]E.R.Rodemich, V.A.Vilnrotter. Digital Combining-Weight Estimation for Broadband Sourcesusing Maximum-Likelihood Estimations [R]. TDA Progress Report42-116, Jet PropulsionLaboratory Pasadena California, pp.98-109. February1994.
[18]V. A. Vilnrotter, E. R. Rodemich, A Real-Time Signal Combining System for Ka-Band FeedArrays Using Maximum Likelihood Weight Estimates [R], TDA Progress Report42-100, JetPropulsion Laboratory Pasadena California, pp.81-99. February1990.
[19]H.H.Tan, R.Liang and P.H.Suen, Optimum Combining of Residual Carrier Array Signals inCorrelated Noises[R], TDA Progress Report42-124, pp.33-52,February1996.
[20]R. Navarro and J. Bunton，Signal Processing in the Deep Space Array Network[R]，IPN ProgressReport42-157. P1-17. May15,2004.
[21]Sierra-Perez M, Torres A, Masa J L, Gomez M, Montesinos I. GEODA: Adaptive Antenna Arrayfor Satellite Signal Reception [C]. The Second European Conference on Antennas andPropagation2007(EuCAP2007). Edinburgh.11-16Nov.2007:1-4
[22]王骞.深空网中给予自适应算法的天线组阵技术研究[D].郑州:解放军信息工程大学,2008.
[23]Victor.A.Vilnrotter, Eugene R.Rodemich, and Samuel J.Dolinar,Jr. Real-Time Combining ofResidual Carrier Array Signals Using ML Weight Estimates[J]. IEEE Transations onCommunications, Vol.40, No.3, March1992.:604-615.
[24]Guolong He. Analysis of Feed Defocus s Effects on a Ka-Band Parabolic Antenna [J].Electrical Engineering,2013(187):189-196
[25]沈彩耀,于宏毅,顾宇魏等.一种用于天线组阵的合成权值盲估计算法[J].计算机应用研究.2010,27(10):3747-3749.
[26]John Toon, Data from Space: Adaptive Array Network Could Improve Access to NASA's EarthObserving Satellites [EB]. http://gtresearchnews.gatech.edu/newsrelease/adaptive-array.htm, May23,2005.
[27]史学书,王元钦.深空组阵EIGEN算法及其低信噪比合成性能分析[J].宇航学报.2009,30(6):2347-2353.
[28]Jun Cheng, Huijing Dou, Qian Lei, Wenxue Li. The Research on Performances of Dual ReceiversPositioning System [C]. International Conference on Information Technology and SoftwareEngineering,2013(211):227-235
[29]Jianxin Wu, Tong Wang, Zheng Bao. Angle Estimation for Adaptive Linear Array usingPCA-GS-ML Estimator [J]. IEEE Transactions on Aerospace and Electronic Systems. Jan.2013(49):670-677
[30]Kay S, Vankayalapati N. Improvement of TDOA Position Fixing Using the Likelihood Curvature[J]. IEEE Transactions on Signal Processing, April15,2013(61):1910-1914
[31]Hara S, Anzai D, Yabu T, Kyesan Lee, Derham T, Zemek R. A Perturbation Analysis on thePerformance of TOA and TDOA Localization in Mixed LOS/NLOS Environments [J]. IEEETransactions on Communications. February2013(61):679–689
[32]Gang Wang, Youming Li, Ansari N. A Semidefinite Relaxation Method for Source LocalizationUsing TDOA and FDOA Measurements [J]. IEEE Transactions on Vehicular Technology, Feb.2013(62):853–862
[33]束锋,朱伟强,陆锦辉,韩玉兵.基于最大比合并的最大似然TDOA/FDOA联合定位方法[J].宇航学报.2010,04:1143-1148
[34]Zheng Yang, Chenshu Wu, Tao Chen, Yiyang Zhao, Wei Gong, Yunhao Liu. Detecting OutlierMeasurements Based on Graph Rigidity for Wireless Sensor Network Localization [J]. IEEETransactions on Vehicular Technology, Jan2013(62):374-383
[35]Yi Jiang, Qing Hu, Dong Kai Yang. Hybrid TOA-TDOA Positioning Algorithm in AIS [J]. AdvancedMaterials Research, January,2013(655-657):876-881
[36]Wang Yue, Liyang Rui, Wenjia Shi, Ho K C, Shang Y. Localization of an acoustic source usingsmart phones [C].2013IEEE Consumer Communications and Networking Conference (CCNC), LasVegas, NV, USA,11-14Jan.2013:761-764
[37]Ying Liu, Xu Zhang, Dan Liu, Zhi Qiang Han. Study of Location Algorithm for Wireless SensorNetworks Based on Newton Iteration [J]. Advanced Materials Research, January,2013(645):285-289
[38]Annibale P, Filos J, Naylor P A, Rabenstein R. TDOA-Based Speed of Sound Estimation forAir Temperature and Room Geometry Inference [J]. IEEE Transactions on Audio, Speech, andLanguage Processing, Feb.2013(21):234-246
[39]Das S, Saha M. Position verification in vehicular communications [C].2013FifthInternational Conference on Communication Systems and Networks (COMSNETS), Bangalore,7-10Jan2013:1-2
[40]Salvati D, Canazza S. Adaptive Time Delay Estimation Using Filter Length Constraints forSource Localization in Reverberant Acoustic Environments [J]. IEEE Signal ProcessingLetters, May2013(20):507-510
[41]Xiong Dong Ding, Guang Qian Chu. The Application of Kalman Filtering on Vessel PositioningSystem [J]. Applied Mechanics and Materials, December,2012(263-266):501-505
[42]Jeich Mar, Shin Ru Wu, Yu Ting Wang, Kuei Chou Tsai. A Three-Dimensional Position SystemUsing Digital TDE Receiver and Cylindrical Array Antenna [J]. Applied Mechanics andMaterials. January,2013(284-287):2603-2608
[43]曾雄飞,孙贵青,黄海宁.水下目标方位估计的克拉美-罗界研究[J].电子与信息学报,2013,1(35):92-98
[44]常硕.深空探测天线高精度控制方法研究[J].测控技术.2012,9(31):64-66
[45]Long Zhang, Xianwei Zhou, Jiwen Guo. Bandwidth Constraint in Intermittently Connected DeepSpace Information Networks Under Scheduled Contacts [J]. Wireless Personal Communications.February2013(68):1255-1285
[46]孔德庆,施浒立.非均匀天线组阵SUMPLE权值信噪比及信号合成性能分析[J].宇航学报.2009.30(5):1941-1946.
[47]孔德庆,施浒立.非均匀天线组阵SUMPLE合成相位补偿及信号强度估计[J].电波科学学报.2009.24(4):632-636.
[48]Tyler Hill, Josh Berk, Jeremy Straub, Jonathan Schiralli, Brian Badders, and Nicholas J.Long. Deep Space Planetary Exploration Using Commercially Available Solar ElectricPropulsion [C].44th Lunar and Planetary Science Conference. The Woodlands, Texas. Jan.2013
[49]李兆训,胡捍英.双瑞利衰落信道下双天线接收系统分集特性分析[J].西安电子科技大学学报.2011,04:160-166
[50]李兆训,曹文魁,梁波,胡捍英.双瑞利衰落信道下最大比合并性能研究[J].电路与系统学报,2012,03:88-93
[51]Taluja P S, Hughes B L. Diversity Limits of Compact Broadband Multi-Antenna Systems [J].IEEE Journal on Selected Areas in Communications, February2013(31):326-337
[52]G Mariotti, P Tortora. Experimental validation of a dual uplink multifrequency dispersivenoise calibration scheme for Deep Space tracking [J]. Electrical&Electronics Engineering.22MAR2013
[53]N S Kardashev, Y Y Kovalev, K I Kellermann. An Earth-Space Radio Interferometer with a350,000km Baseline [J]. The Radio Science Bulletin No.343, December2012:22-29
[54]Samudra E Haque, Jeremy Straub, and David Whalen. Small Satellites with Micro-Propulsionfor Communications with the Lunar South Pole Aitkens Basin [C],2013IEEE AerospaceConference, March2013:1-12
[55]王勤民,张忠培,刘明,结凤克.空间分集干扰对齐研究[J].系统工程与电子技术.2013,01:179-183
[56]殷勤业,贾曙乔,左莎琳,董柳青,尹诗媛.分布式多天线跳空收发技术(Ⅰ)[J].西安交通大学学报.2013,01:1-6
[57]李洪烈,周洪霞,刘辉传.改善系统中断速率的一种新虚拟接收天线方案[J].电讯技术.2013,01:88-92
[58]朱婕,郭业才.基于最大比合并空间分集判决反馈盲均衡算法[J].系统仿真学报,2008,11:2843-2845
[59]Singh H S, Bharti P K, Pandey G K, Meshram M K. A compact tri-band MIMO/diversity antennafor mobile handsets [C].2013IEEE International Conference on Electronics, Computing andCommunication Technologies (CONECCT), Bangalore,17-19Jan.2013:1-6
[60]S. Vongsack, C. Phongcharoenpanich, S. Kosulvit, K. Hamamoto, and T Wakabayashi.Unidirectional antenna using two-probe excited circular ring above square reflector forpolarization diversity with high isolation [J], Electromagnetics Research,2013(133):159-176.
[61]万明康,罗柏文，于宏毅等.相关噪声条件下的多天线信号合成性能分析[J].计算机工程与应用.2010,46(32):12-14.
[62]D. H. Rogstad. The SUMPLE Algorithm for Aligning Arrays of Receiving Radio Antennas:Coherence Achieved with Less Hardware and Lower Combining Loss[R]. IPN Progress Report42-162. pp:1-29. August15,2005.
[63]Shivali Goel and lemal H. Abawajy. Performance of Smart Antennas With Receive Diversityin Wireless Sensor Networks [C]. IEEE-lnternatiOl;al Conference on Signal processing,Communications and Networking Madras Instlillte o/Technology, Anna University Chel1l1GiIndia, Jan4-6,2008,:272-278.
[64]Dau-Chyrh Chang, Cheng-Nan Hu. Smart Antennas for Advanced Communication Systems [J].Proceedings of the IEEE. July2012,(100):2233–2249.
[65]Hang Wong, Kwai-Man Luk, Chi Hou Chan, Quan Xue, Kwok Kan So, Hau Wah Lai. SmallAntennas in Wireless Communications [J]. Proceedings of the IEEE. July2012,(100):2109–2121.
[66]Y.M.Madany, H.M.Elkamchouchi, A.A.Salama. Design and analysis of optimum miniaturizedconformal smart antenna system using1×8switched Butler Matrix for wireless applications
[C].2012International Conference on Microwave and Millimeter Wave Technology (ICMMT).5-8May2012,(1):1–4.
[67]Y Rama Krishna, P E S N.Krishna Prasad, P V Subbaiah and B Prabhakara Rao. A Hybrid Modelof CLMS and ACLMS Algorithms for Smart Antennas [C]. Fourth International Conference onNetworks&Communications, New York,2013:93-104
[68]M A Soliman, T E Taha, W E Swelam and A M Gomaa.3.5/5GHz dual-band8x8adaptive arrayantenna [R], Electromagnetics Research C,2013(34):85-98.
[69]Vaibhav Narula, Rajat Srivastava. Feedforward Feedback (FFFB) Method for Dereferencingthe Non Blind Algorithms in Adaptive Beamforming [J]. Communications in Computer andInformation Science,2013(296):207-214
[70]Gary K, W Wong, Xiaohua Jia. An efficient scheduling scheme for hybrid TDMA and SDMA systemswith smart antennas in WLANs [J]. Wireless Networks, February2013(19):259-271
[71]Xuezhi Yang, Wei Jiang, Vucetic B. A Random Beamforming Technique for OmnidirectionalCoverage in Multiple-Antenna Systems [J]. IEEE Transactions on Vehicular Technology, March2013(62):1420-1425
[72]M Agatonovic, Z Stankovic, I Milovanovic, N Doncov, L Sit, T Zwick and B Milovanovic.Efficient neural network approach for2D DOA estimation based on antenna array measurements[J]. Electromagnetics Research,2013(137):741-758.
[73]Kerem Kucuk, Adnan Kavak. Connectivity Analysis for Wireless Sensor Networks with AntennaArray Integrated Central Node [J]. Wireless Personal Communications. March2013:1-10
[74]M F Jamlos, M R Kamarudin, M F Malek, M Jusoh, M A Jamlos. Smart receiver system of greenhybrid antennas [J]. Microwave and Optical Technology Letters, April2013(55):800–809
[75]Jijiang Chen, Jing Zhou, Jianfeng Lu, Research on the scheme and performance of linearSA in CDMA application [C].201315th International Conference on Advanced CommunicationTechnology (ICACT), PyeongChang, Korea (South),27-30Jan.2013:107-115
[76]王鼎,王超,吴瑛.幅相误差对MUSIC算法空域谱及分辨性能影响的分析[J].通信学报,2010,(4):55-63.
[77]赵瑞锋，谈振辉，蒋海林.无线系统中的智能天线[J].电子学报.2000,28(12):77-80.
[78]漆雪梅,沈彩耀,张效义. SIMO信道中基于奇异值分解的盲信噪比估计算法[J].信号处理.2011,27(4):552-557.
[79]吴湛击,高翔.一种新的高效自适应的联合编码调制分集的多入多出系统[J].电子学报.2013,01:131-137
[80]Lehmann, F. Joint Channel Estimation and Decoding for Trellis-Coded MIMO Two-Way RelayNetworks [J]. IEEE Journal on Selected Areas in Communications,2012,11(PP):1-14
[81]Kanno, Issei; Amano, Yoshiaki; Yamaguchi, Akira; Experimental evaluation of adaptiveimpedance control for MIMO antennas in an FDD-LTE terminal [C].2013IEEE Radio and WirelessSymposium (RWS), Austin, TX, USA.20-23Jan.2013:373–375
[82]Horia Vlad Balan, Ryan Rogalin, Antonios Michaloliakos, Konstantinos Psounis, GiuseppeCaire [C]. Achieving high data rates in a distributed MIMO system. Mobicom '12Proceedingsof the18th annual international conference on Mobile computing and networking. New York,NY, USA.2012,:41-52.
[83]姜永权,刘乃安,沈民奋. MIMO系统一种新的功率分配算法及容量分析[J].电子学报.200735(9):1749-1753.
[84]漆雪梅,张效义. SIMO信道中信噪比估计算法分析与仿真[J].计算机工程.2011.
[85]Bing Chen, Xiaoping Liu, Kefu Liu, Chong Lin. Adaptive fuzzy tracking control of nonlinearMIMO systems with time-varying delays [J]. Fuzzy Sets and Systems, April,2013(217):1-21
[86]Zeng X, Li Q. Zhang Q, Qin J. Joint Beamforming and Antenna Subarray Formation for MIMOCognitive Radios [J]. IEEE Signal Processing Letters, May2013(20):479-482
[87]Ayfer Ozgur, Olivier Leveque, David Tse. Spatial Degrees of Freedom of Large DistributedMIMO Systems and Wireless Ad hoc Networks [J]. IEEE Journal on Selected Areas inCommunications, February2013(31):202-214
[88]Brady J, Behdad N, Sayeed A. Beamspace MIMO for Millimeter-Wave Communications: SystemArchitecture, Modeling, Analysis, and Measurements [J]. IEEE Transactions on Antennas andPropagation.22May2013(PP):1
[89]Kumar Rakesh. A fully integrated2×2b/g and1×2a-band MIMO WLAN SoC in45nm CMOS formulti-radio IC [C].2013IEEE International Solid-State Circuits Conference Digest ofTechnical Papers (ISSCC), San Francisco, CA, USA.17-21Feb.2013:328-329
[90]Hoydis J, ten Brink S, Debbah M. Massive MIMO in the UL/DL of Cellular Networks: How ManyAntennas Do We Need?[J]. IEEE Journal on Selected Areas in Communications, February2013(31):160-171
[91]Rusek F, Persson D, Buon Kiong Lau, Larsson E G, Marzetta T.L, Edfors O, Tufvesson F. ScalingUp MIMO: Opportunities and Challenges with Very Large Arrays [J]. IEEE Signal ProcessingMagazine, Jan.2013(30):40-60
[92]K. Tiels, P.S.C.Heuberger, J. Schoukens. Parameter reduction of MISO Wiener-Schetzenmodels using the best linear approximation [C]. Instrumentation and Measurement TechnologyConference (I2MTC).13-16May2012:2114–2118.
[93]John G.Proakis,张力军，张宗橙，郑宝玉.数字通信[M].第四版.北京：电子工业出版社.2003：629-637.
[94]Zijian Wang, Wen Chen, Jun Li. Efficient Beamforming for MIMO Relaying Broadcast ChannelWith Imperfect Channel Estimation [J]. IEEE Transactions on Vehicular Technology. Jan.2012,(61):419–426.
[95]Sang-Hyeong Kim, Zhe-Jun Jin, Yoon-Byung Chae, and Tae-Yeoul Yun. Small Internal AntennaUsing Multiband, Wideband, and High-Isolation MIMO Techniques [J]. ETRI Journal, February2013(35):51-57
[96]Ozgur A, Packard Electr, Leveque O, Tse D. Spatial Degrees of Freedom of Large DistributedMIMO Systems and Wireless Ad Hoc Networks [J]. IEEE Journal on Selected Areas inCommunications, February2013(31):202-214
[97]Alex Stéphenne, Faouzi Bellili, and Sofiène Affes. Moments-Based SNR Estimation overLinearly Modulated Wireless SIMO Channels[J]. IEEE Transactions on WirelessCommunications,2010,9(2):714-722.
[98]N.Aboutorab, W.Hardjawana, B.Vucetic. A New Iterative Doppler-Assisted ChannelEstimation Joint With Parallel ICI Cancellation for High-Mobility MIMO-OFDM Systems [J].IEEE Transactions on Vehicular Technology. May2012,(61):1577–1589.
[99]樊昌信,詹道庸,徐炳祥,吴成柯.通信原理[M].第四版.北京:国防工业出版社.2001:22-26.
[100] S.J.Dolinar. Cramer-Rao Bounds for Signal-to-Noise Ratio and Combiner WeightEstimation [R]. TDA Progress Report42-86, Jet Propulsion Laboratory Pasadena California,April-June1986,:124-130.
[101] Shen Caiyao, Yang ke, Yu Hongyi, Zhang Tianzhong. Crame-Rao Bands of SNR Estimationfor BPSK and QPSK Signals over SIMO Channels [C]. Proceedings of20102nd InternationalConference on Signal Processing Systems, Dalian China. July5-7,2010:647-650.
[102] E.R.Rodemich, V.A.Vilnrotter. Digital Combining-Weight Estimation for BroadbandSources using Maximum-Likelihood Estimations [R]. TDA Progress Report42-116, JetPropulsion Laboratory Pasadena California, February1994,:98-109.
[103] V. A. Vilnrotter, E. R. Rodemich. A Real-Time Signal Combining System for Ka-Band FeedArrays Using Maximum Likelihood Weight Estimates [R], TDA Progress Report42-100, JetPropulsion Laboratory Pasadena California, February1990,:81-99.
[104]隋丹,葛临东.一种新的基于改进PASTd的中频信号盲信噪比估计算法[J].电子与信息学报.2007,29(7):1657-1661.
[105]张海瑛,袁超伟.一种实用的通信信号信噪比盲估计方法[J].无线电通信技术.2007,33(2):49-51.
[106]彭耿,黄知涛,陆凤波,姜文利.中频通信信号信噪比的快速盲估计[J].电子与信息学报.2010,32(1):102-106.
[107]沈彩耀,于宏毅,顾宇魏,杨珂.一种用于天线组阵的合成权值盲估计算法[J].计算机应用研究.2010,27(10):3747-3749.
[108]万明康,罗柏文，于宏毅,杨珂.相关噪声条件下的多天线信号合成性能分析[J].计算机工程与应用,2010,46(32):12-14.
[109] H.H.Tan, R.Liang and P.H.Suen, Optimum Combining of Residual Carrier Array Signalsin Correlated Noises [R], TDA Progress Report42-124, Jet Propulsion Laboratory PasadenaCalifornia, February1996,:33-52.
[110]史学书,王元钦.深空组阵Eigen算法及其低信噪比合成性能分析[J].宇航学报.2009,30(6):2347-2353.
[111] Cheung K M. Eigen theory for optimal signal combining: a unified approach [R]. TDAProgress Report,1996,42(126C):1-9.
[112] Simo Doclo, Marc Moonen. Robust Time-Delay Estimation in Highly Adverse AcousticEnvironments [C]. IEEE Workshop on Applications of Signal Processing to Audio andAcoustics2001, New Paltz, New York, October2001,:59-62.
[113] C.H.Lee, V.Vilnrotter, E.Satorius, Z.Ye, D.Fort, and K.-M. Cheung, Large-Array SignalProcessing for Deep Space Applications [R]. JPL Interplanetary Network Progress Report42-150, Jep Propulsion Laborator, Pasadena, CA, August15,2002,:1-28.
[114] Charles H.Lee, Kar-Ming Cheung and Victor A.Vilnrotter. Fast Eigen-based SignalCombining Algorithms for Large Antenna Arrays [C]. IEEE Aerospace Conference2003Paper.2003,1220(2):1123-1129.
[115] Shi Xueshu; Wang Yuanqin. Performance of Deep Space Seetion Front Eigen CombiningAlgorithm in Low SNR Condition [J]. Journal of Astronautics.2009,30(6):2347-2353.
[116] B Luo, H Yu, X Zhang, Z Shen and Q Li. On Eigen-based Signal Combining Using theAutocorrelation Coefficient [J]. IET Communications, Volume6, issue18, December2012,p.3091-3097
[117] Shen Caiyao, Yang Ke, Yu Hongyi. Cramer-Rao Bounds of SNR Estimation for BPSK and QPSKSignals in Antenna Arraying[C]. In Proc. the2nd International Comference in SignalProcessing Systems (ICSPS'2010). DaLian, China,2010:647-650.
[118]沈彩耀,于宏毅.多天线PSK信号合成权值估计的CRLB研究[J].数据采集与处理.2011,25(S):106-109.
[119]沈智翔.多天线信号合成关键技术研究与实现[D].郑州:信息工程大学.2010.
[120]杨珂,王丽,李鸥,于宏毅.基于最小二乘的天线组阵合成权值及信噪比盲估计[J].电路与系统学报.2011,16(3):123-128.
[121]沈彩耀,于宏毅,顾宇巍等.一种用于天线组阵的合成权值盲估计算法[J].计算机应用研究.2010,27(10):3747-3749.
[122]张贤达，现代信号处理（第二版）[M]，清华大学出版社，北京，2003年3月，:281-282.
[123] Arobbins H, Monroe S. A stochastic approximation method [C]. Annas of MathematicalStatistics,1951,22,:400~407.
[124] Widrow B, Stearns S D. Adaptive Signal Processing [M]. New York: Prentice-Hall,1985.
[125]秦洪峰.水下多目标定位关键技术研究[D].西安:西北工业大学.2002:27-33.
[126] Kung Yao, Ralph E. Hudson, Chris W. Reed, Daching Chen, and Flavio Lorenzelli. BlindBeamforming on a Randomly Distributed Sensor Array System [J], IEEE Journal on selectedareas in communications,1998,16(8):1555-1566.
[127] Roenko, D.Kurkin, V.Lukin, I.Djurovic. Time delay estimation for noise-like widebandsignals in non-Gaussian environment [C].2012Mediterranean Conference on EmbeddedComputing (MECO).19-21June2012,:100–103.
[128] Wenhong Liu, Tianshuang Qiu, Zhiyue Lin and Richard W. McCallum. Estimation ofPropagation Velocity of Gastric Electrical Activity Using LMSTDE [C]. Engineering inMedicine and Biology27th Annual Conference. Shanghai, China, September1-4,2005,:5954-5957.
[129] AN Liansuo, WANG Peng, JIANG Genshan, LI Gengsheng, SHEN Guoqing. Time Delay Estimationfor Boiler Tube Leak Sound Propagation [J]. Proceeding of the CSEE2012,32(2):16-23.
[130] Jing wen, Ping Li, Yumei Wen, Unbiased Adaptive Time-delay Estimation in NoisyEnvironments [C], ICARCV '06.9th International Conference on Control, Automation,Robotics and Vision.5-8Dec.2006,:1-6.
[131] Ping Li, Yumei Wen, Jing Wen. Time delay estimation method for leak location of buriedwater distribution pipes [C]. Proceedings of the7th World Congress on Intelligent Controland Automation. Chongqing, China. June25-27,2008,:3052-3056.
[132] Huijuan Wu, Yumei Wen, Jin Yang, and Ping Li. Adaptive detection and location for waterpipeline leaks in lower SNR and nonstationary environments [C].2009IEEE InternationalConference on Control and Automation Christchurch, New Zealand. December9-11,2009.:1318-1323.
[133] M.Rotoloni, S. Tomasin, L.Vangelista. Maximum Likelihood Estimation of Time andCarrier Frequency Offset for DVB-T2[J]. IEEE Transactions on Broadcasting, March2012,(58):77–86.
[134] Pushpendra Singh and Pradip Sircar. Time delays and angles of arrival estimation usingknown signals [J]. Signal, Image and Video Processing.2012,(6):171-178.
[135] Daegun Oh, Myung-kyun Kwak, Jong-Wha Chong. Subspace-Based Propagation DelayEstimation Robust to Frequency Offset for Two-Way Ranging [J]. IEEE Communications Letters,February2012,(16):216–219.
[136] Jean Pierre Delmas, Yann Meurisse. On the Cramer Rao bound and maximum likelihoodin passive time delay estimation for complex signals [C].2012IEEE InternationalConference on Acoustics, Speech and Signal Processing (ICASSP),25-30March2012,:3541–3544.
[137] Jingdong Chen, Jacob Benesty and Yiteng Huang. Robust time delay estimation exploitingredundancy among multiple microphones [J], IEEE Trans. Acoustic, Speech, Signal Processing,2003,11(6):549-557.
[138] Jacob Benesty, Jingdong Chen and Yiteng Huang. Time-Delay Estimation via LinearInterpolation and Cross Correlation[J]. IEEE Trans on Speech and Audio Processing.2004,12(5):509-519.
[139] Jingdong Chen, Yiteng (Arden) Huang and Jacob Benesty. Time Delay Estimation viaMultichannel Cross-Correlation[J]. ICASSP2005:49-52.
[140] Jacob Benesty, Yiteng Huang and Jingdong Chen. Time Delay Estimation via MinimumEntropy[J].IEEE Signal Processing Letters.2007,14(3):157-160.
[141] Jacob Benesty, Yiteng Huang and Jingdong Chen. Laplace Entropy and its Applicationto Time Delay Estimation for Speech Signals[J]. ICASSP2007:113-116.
[142] R. L. Kirlin, D. F. Moore and R. F. Kubichek. Improvement of delay measurements fromsonar arrays via sequential state estimation[J]. IEEE Trans. Acoust., Speech, SignalProcessing.1981,29(3):514–519.
[143] T. Nishiura, T. Yamada, S. Nakamura, and K. Shikano. Localization of multiple soundsources based on a CSP analysis with a microphone array [C]. Proc.IEEE ICASP,2000,:1053-1055.
[144] S. M. Griebel and M. S. Brandstein. Microphone array source localization usingrealizable delay vectors [J]. in Proc. IEEE Workshop on Applications of Signal Processingto Audio and Acoustics,2001:71-74.
[145] Huang Y, Benesty J, Elko G W. Adaptive Eignvalue Decomposition Algorithm for RealtimeAcoustic Source Localization System [C].IEEE International Conference on Acoustic, Speechand Signal Processing. Seatle, WA, USA,1998,2:937-940.
[146] Huang Y, Benesty J. Adaptive Blind Channel Identification: Multi-Channel Least MeanSquare and Newton Algorithms[C]. Proceedings of the IEEE Conference on Acoustic, Speechand Signal Processing. Orlando, florida, USA,2002,2:937-940.
[147] Jingdong Chen, Yiteng Huang and Jacob Benesty. An Adaptive Blind SIMO IdentificationApproach to Joint Multichannel Time Delay Estimation [J]. ICASSP2004. pp.53-56.
[148] Yiteng (Arden) Huang, and Jacob Benesty. A Class of Frequency-Domain AdaptiveApproaches to Blind Multichannel Identification[J]. IEEE Trans on Signal Processing.2003,51(1):11-24.
[149] H.C.So, P.C.Ching and Y.T.Chan. A new algorithm for explicit adaptation of time delay[J]. IEEE Transactions on Signal Processing, July1994,(42):1816-1820.
[150] H.C.So. Fast algorithm for nonstationary delay estimation [J]. Electronics Letters,April1998,(34):722-723.
[151] H.C.So and P.C.Ching. Performance analysis of ETDGE-an efficient and unbiased TDOAestimator for source localization [J]. IEE Proceedings Radar, Sonar and Navigation,December1998,(145):325-330.
[152] H.C.So and P.C.Ching. A comparative study of five LMS-based adaptive algorithms fornonstationary delay estimation [J]. IEE Proceedings Radar, Sonar and Navigation, Feb.2001,(148):9-15.
[153] Zheng Cheng, Tjeng Thiang Tjhung. A new time delay estimator based on ETDE [J]. IEEETransactions on Signal Processing. July2003:1859-1869.
[154] S. R. Dooley and A. K. Nandi. Adaptive time delay and frequency estimation for digitalsignal synchronization in CDMA systems [J]. in Conf. Rec. Thirty-Second Asilomar Conf.Signals, Syst., Comput.1998,(2):1838-1842.
[155]罗柏文,于宏毅.采用合成方法的多路信号自适应时延联合估计[J].信号处理.2013，2(29):159-164
[156] C. Shen and H. Yu. Time-delay alignment technique for a randomly distributed sensorarray [J]. IET Communications.2011,5(8):1068-1072.
[157] Lovell B C, Williamson R C. The statistical performance of some instantaneous frequencyestimators [J]. IEEE Trans on Acoustics, Speech, Signal Processing,1992,40(7):1708-1723.
[158] Hua Jingyu, You Xiaohu. Doppler shift estimation methods in mobile communicationsystems [J]. Journal of Southeast University(English Edition), Dec.2004,(20):405-411.
[159] D.W.Tufts and C.D.Melissinos. Simple, effective computation of principal eigenvectorand their eigenvalues and application to highresolution estimation of frequencies [C].Proc. Int. Conf. Acoust., Speech, Signal Processing, Mar.1985,:951-954.
[160] D. M. Etter and D. R. Hush. A new technique for adaptive frequency estimation andtracking [J]. IEEE Trans. ASSP,1987,(35):561–564.
[161] S.R.Dooley and A.K.Nandi. Adaptive time delay and Doppler shift estimation fornarrowband signals [J]. IEE Proc. Radar, Sonar Navig. Ocotber1999,(146):243-250.
[162] Zheng Cheng and T.T.Tjhung. Explicit frequency estimation and tracking using modulatedlagrange delay filter [C].2005Asia-Pacific Conference on Communications. Perth, WesternAustralia,3-5October2005,:916-920.
[163] Z. Cheng and T. T. Tjhung. Accurate explicit frequency estimation using modulatedlagrange delay filter [C]. The2nd IEEE International Symposium on Signal Processing andInformation Technology. Marrakech, Morocco, December2002,:634–639.
[164] R. Punchalard, J. Koseeyaporn, P. Wardkein. Indirect frequency estimation based onsecond-order adaptive FIR notch filter [J], Signal Processing.2009,89(7):1428–1435.
[165] H.C.So and P.C.Ching. Adaptive algorithm for direct frequency estimation [J]. IEEProceedings-Radar, Sonar and Navigation, December2004,6(151):359-364.
[166] T.Pitschel, H.C.So and J.Zheng. New adaptive algorithm for unbiased and directestimation of sinusoidal frequency [J]. IEICE Transactions on Fundamentals of Electronics,Communications and Computer Sciences, March2008,3(E91-A):872-874.
[167] Y.Wu, H.C.So and Y.Tan. Joint time-delay and frequency estimation using parallelfactor analysis [J]. Signal Processing. August2009,(89):1667-1670.
[168] K.W.K.Lui and H.C.So. Time delay estimator based on frequency estimation approach [J].IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences.June2010,6(E93-A):1248-1250.
[169] F.K.W.Chan, H.C.So, W.H.Lau and C.F.Chan. Structured total least squares approach forefficient frequency estimation [J]. Signal Processing. April2011,4(91):1043-1047.
[170]杨珂.随机布局多天线信号合成中的多参数联合盲估计研究[D].郑州:解放军信息工程大学,2011.
[171] Nakagawa T, Matsui M, Kobayashi T, et.al. Non-data-aided wide-range frequency offsetestimator for QAM optical coherent receivers[C]. Optical Fiber Communication Conferenceand Exposition and the National Fiber Optic Engineers Conference[C],2011:1-3.
[172] Sergienko A. B, Petrov A. V. Joint blind estimation of carrier phase and frequencyoffset for QAM signals using circular harmonic decomposition [C]. IEEE InternationalConference on Acoustics, Speech and Signal Processing.2011:22-27.
[173] Xian Zhou, Xue Chen, Keping Long. Wide-Range Frequency Offset Estimation Algorithmfor Optical Coherent Systems Using Training Sequence [J]. IEEE Photonics TechnologyLetters, Jan.12012,(24):82–84.
[174] Hoon Huh, Tulino A.M., Caire, G. Network MIMO With Linear Zero-Forcing Beamforming:Large System Analysis, Impact of Channel Estimation, and Reduced-Complexity Scheduling[J]. IEEE Transactions on Information Theory. May2012,(58):2911–2934.
[175] Zhi Kun Song, Rui Zhe Yang, Li Zhang, Peng Bo Si and Yan Hua zhang. Multi-model BasedChannel Estimation for Doubly Selective Wireless Fading Channels [J]. Communications inComputer and Information Science.2012,(288):515-525.
[176] Ming Jiang, Siji Huang,Wenkun Wen. Adaptive Polar-Linear Interpolation Aided ChannelEstimation for Wireless Communication Systems [J]. IEEE Transactions on WirelessCommunications. March2012,(11):920–926.
[177] Kai-Jie Yang, Chin-Liang Wang, Yuh-Ren Tsai. Joint Channel and Doppler SpreadEstimation over Time-Varying Flat-Fading Channels [C]. In2012IEEE75th VehicularTechnology Conference (VTC Spring),6-9May2012,:1–5.
[178] Lioliou, P. Viberg, M. and Matthaiou, M. Bayesian Approach to Channel Estimation forAF MIMO Relaying Systems [J]. IEEE Journal on Selected Areas in Communications. September2012,(30):1440–1451.
[179] Abdallah, S. Psaromiligkos, I.N. Blind Channel Estimation for Amplify-and-ForwardTwo-Way Relay Networks Employing M-PSK Modulation [J]. IEEE Transactions on SignalProcessing. July2012,(60):3604–3615.
[180] Carrasco-Alvarez, R, Parra-Michel, R., Orozco-Lugo, A.G., Tugnait, J.K.Time-Varying Channel Estimation Using Two-Dimensional Channel Orthogonalization andSuperimposed Training [J]. IEEE Transactions on Signal Processing. Aug.2012,(60):4439–4443.
[181] Medbo, J. Harrysson, F. Efficiency and accuracy enhanced super resolved channelestimation [C].20126th European Conference on Antennas and Propagation (EUCAP).26-30March2012:16–20.
[182] H.C.So. Spontaneous and Explicit Estimation of Time Delays in the Absence/Presenceof Multipath Propagation [D]. Department of Electronic Engineering, The Chinese Universityof Hong Kong,1995.
[183] Haykin. S. Adaptive Filter Theory, Fourth Edition [M]. Englenwood Cliffs, NJ:Pretice-Hall,2002.
[184] Ching, P.C., Chan, Y.T. Adaptive time delay estimation with constraints [J]. IEEETransactions on Acoustics, Speech and Signal Processing. April1988,:599-602.
[185] K.Sam Shanmugan and A.M.Breipohl, Random Signals: Detection, Estimation and DataAnalysis [M], John Wiley&Sons, Inc.,1998.
[186] I.S.Gradshtegn and I.M.Ryzhik, Tables of Tntegrals, Series and Products [M], New York,Academic Press.1980,:7.
[187] Xian Zhou, Keping Long, Rui Li, Xiaolong Yang and Zhongshan Zhang. A simple andefficient frequency offset estimation algorithm for high-speed coherent optical OFDMsystems [J].2012OSA.26March2012,(20):7350-7361.
[188] H.Saeedi-Sourck, Yan Wu, J.W.M.Bergmans, S.Sadri, B.Farhang-Boroujeny.Low-Complexity Carrier Frequency Offset Estimation for Multiuser Offset QAM Filter BankMulticarrier Systems Uplink [C].2012IEEE75th Vehicular Technology Conference (VTCSpring),6-9May2012,:1–5.
[189] Bowen Luo and Hongyi Yu. The adaptive Frequency and Phase Shift Compensator [C].ISAI2010. Chengdu China,2010,(1):272-274.
[190]罗柏文，万明康，于宏毅.两种基于自适应相位补偿的FDOA估计算法[J].数据采集与处理，2012,(1):20-26.
[191]陈彩莲.随机分布多天线组阵的关键技术研究与实现[D].郑州:解放军信息工程大学,2010.
[192]张建业.多天线接收中的自适应参数估计与合成算法研究及实现[D].郑州:解放军信息工程大学,2011.
[193] D. H. Rogstad, A. Mileant, and T. T. Pham. Antenna Arraying Techniques in the DeepSpace Network [M], Hoboken, New Jersey. John Wiley2003:73-126.
[194] National Semiconductor Corp. Microphone array amplifier reduces background noise [EB].http://mobiledevdesign.com/standards_regulations/microphone-array-amplifier-national-semiconductor-0228/index.html. Feb28,2008.
[195] Kung Yao, Ealph E.Hudson, Chris W.Reed, Daching Chen, and Flavio Lorenzelli. BlindBeamforming on a Randomly Distributed Sensor Array System [J], IEEE JOURNAL ON SELECTEDAREAS IN COMMUNICATIONS, OCTOBER1998,8(16):1555-1567.
[196] John Toon. Adaptive Array Network Could Improve Access to NASA's Earth ObservingSatellites [EB]. http://eo1.gsfc.nasa.gov/new/validationreport/AdaptiveArray.html. May23,2005.
[197] S.J.Dolinar and S.Kalson. Maximum Likelihood Estimation of Signal to Noise Ratio andCombiner Weight [R]. TDA Progress Report42-85, Jet Propulsion Laboratory PasadenaCalifornia, January-March1986,:32-41.
[198]冯正权何庆华吴宝明闫庆广高丹丹.基于高阶统计量分析的生物医学信号处理应用[J].中国医学物理学杂志.2011,5:2899-2903.
[199] Hong Tao Sun, Yong Shou Dai, Fang Wang, Xing Peng. Seismic Wavelet Estimation UsingHigh-Order Statistics and Chaos-Genetic Algorithm [J]. Advanced Materials Research.January,2012(433-440):4241-4247.
[200] Valeriu Vrabie, Eric Perrin, Jean-Luc Bodnar, Kamel Mouhoubi, and Vincent Detalle.Active IR Thermography Processing Based on Higher Order Statistics for NondestructiveEvaluation [C].20th European Signal Processing Conference (EUSIPCO2012). Bucharest,Romania, August27-31,2012,:894-898.
[201] Han-Kui Chang, Jia-Chin Lin, Meng-Lin Ku. Sequential spectrum sensing based onhigher-order statistics for cognitive radios [C].2012International Conference onCommunications and Information Technology (ICCIT),26-28June2012,:413–417.
[202] F.J.Lpez-Martnez, E. Martos-Naya, J. F.Paris, U. Fernndez-Plazaola. Higher OrderStatistics of Sampled Fading Channels with Applications [J]. IEEE Transactions onVehicular Technology, Sept.2012,(61):3342–3346.
[203] Yilmaz, F. Alouini, M. On the Computation of the Higher-Order Statistics of theChannel Capacity over Generalized Fading Channels [J]. to appear in IEEE WirelessCommunications Letters.2012,:1–4.
[204] H.H.Tan, R.Liang and P.H.Suen, Optimum Combining of Residual Carrier Array Signalsin Correlated Noises [R], TDA Progress Report42-124, Jet Propulsion Laboratory PasadenaCalifornia, February1996,:33-52.
[205]蔡振浩,于宏毅.基于二阶锥规划法的混合滤波器组最优化设计[J].北京邮电大学学报.2011,34(5):124-127.
[206]蔡振浩,于宏毅.基于加权最小二乘正则化方法的混合滤波器组最优化设计[J].信号处理.2010,26(7):1110-1114.
[207]张珺,魏学业.余弦调制滤波器组的设计研究[J].北方交通大学学报.2004.6,28(3):103-109.
[208]朱婕，郭业才.基于最大比合并空间分集判决反馈盲均衡算法[J].系统仿真学报.2008,20(11):2843-2850
[209]付永明，朱江，琚瑛珏. Gardner定时同步环路参数设计及性能分析[J].通信学报.2012,(6):191-200.
[210]朱雪阳.基于Gardner算法的位定时同步研究[D].南京理工大学.2010.
[211]曾军，黄华，李东.恒模类均衡算法研究[J].通信技术.2009,42(10):60-62.
[212] Shuo Liu, Julian Cheng and Norman. Asymptotic Error Analysis of Diversity Schemes onArbitrarily Correlated Rayleigh Channels[J]. IEEE Trans on Communications.2010,58(5):1351-1355.
[213]朱婕,郭业才.基于最大比合并空间分集判决反馈盲均衡算法[J].系统仿真学报.2008,20(11):2843-2845.
[214] E.moulines, D.Slock, PDuhamel. Subspace Methods for the Blind Identification ofMultichannel FIR Filters [J].IEEE Trans. on signal processing,1995,2(43):516-526.
[2] Potter P, Merrick W, Ludwig A. Large Antenna Apertures and Arrays for Deep SpaceCommunications[R]. JPL Technical Report No.32-848. November1965.
[3] Urech J. Telemetry Improvement Proposal for the85-feet Antenna Network[R]. Space ProgramsSummary37-63,11, JPL, May31,1970.
[4]姚飞，匡麟玲，詹亚锋，陆建华.深空通信天线组阵关键技术及其发展趋势[J].宇航学报.2010，10(31)：2231-2238.
[5]于志坚.深空测控通信系统[M].北京：国防工业出版社，2009:1-11，95-123，291-320
[6] Mileant A, Hinedi S. Overview of arraying techniques for deep space communications[J].IEEE Trans on Comm.,1994,42(2):1856-1865.
[7] Durgadas S. Bagri, Joseph I. Statman, and Mark S. Gatti， Proposed Array-Based Deep SpaceNetwork for NASA，Proceedings of the IEEE，Vol.95, No.10, October2007:1916-1922.
[8]王慧君.天线组阵技术及其在深空网中的应用[C].信息产业部雷达专业第十六届年会论文集，2005,11:269-276.
[9]张乃通，李晖，张钦宇.深空探测通信技术发展趋势及思考[J].宇航学报.2007,7(28):786-793.
[10]叶培建，彭兢.深空探测与我国深空探测展望[J].中国工程科学.2006,10(8):13-18.
[11]李小梅，周三文，李海涛，卢满宏.天线组阵技术验证试验与数据分析[C].第十五届全国遥测遥控技术年会论文集.2008,11:339-344.
[12]徐瑞，李海涛，尚海滨等.利用罗塞塔探测器进行天线组阵试验可行性分析[C].中国宇航学会深空探测技术专业委员会第五届年会论文集.2008,10:53-59.
[13]欧阳自远.2013，中国有望飞往火星—解读中国自主火星探测的路径与难点.2010.10.26.http://pdf.news365.com.cn/whpdf/20101026/WH10102612.pdf.
[14]王骞,于宏毅.深空网中的天线组阵技术及其发展[J].信息工程大学学报.2009,3:365-368.
[15]沈彩耀.多天线信号联合接收的合成技术研究[D]郑州:解放军信息工程大学,2011.
[16]S.J.Dolinar and S.Kalson. Maximum Likelihood Estimation of Signal to Noise Ratio andCombiner Weight [R]. TDA Progress Report42-85, Jet Propulsion Laboratory PasadenaCalifornia, pp.32-41. January-March1986.
[17]E.R.Rodemich, V.A.Vilnrotter. Digital Combining-Weight Estimation for Broadband Sourcesusing Maximum-Likelihood Estimations [R]. TDA Progress Report42-116, Jet PropulsionLaboratory Pasadena California, pp.98-109. February1994.
[18]V. A. Vilnrotter, E. R. Rodemich, A Real-Time Signal Combining System for Ka-Band FeedArrays Using Maximum Likelihood Weight Estimates [R], TDA Progress Report42-100, JetPropulsion Laboratory Pasadena California, pp.81-99. February1990.
[19]H.H.Tan, R.Liang and P.H.Suen, Optimum Combining of Residual Carrier Array Signals inCorrelated Noises[R], TDA Progress Report42-124, pp.33-52,February1996.
[20]R. Navarro and J. Bunton，Signal Processing in the Deep Space Array Network[R]，IPN ProgressReport42-157. P1-17. May15,2004.
[21]Sierra-Perez M, Torres A, Masa J L, Gomez M, Montesinos I. GEODA: Adaptive Antenna Arrayfor Satellite Signal Reception [C]. The Second European Conference on Antennas andPropagation2007(EuCAP2007). Edinburgh.11-16Nov.2007:1-4
[22]王骞.深空网中给予自适应算法的天线组阵技术研究[D].郑州:解放军信息工程大学,2008.
[23]Victor.A.Vilnrotter, Eugene R.Rodemich, and Samuel J.Dolinar,Jr. Real-Time Combining ofResidual Carrier Array Signals Using ML Weight Estimates[J]. IEEE Transations onCommunications, Vol.40, No.3, March1992.:604-615.
[24]Guolong He. Analysis of Feed Defocus s Effects on a Ka-Band Parabolic Antenna [J].Electrical Engineering,2013(187):189-196
[25]沈彩耀,于宏毅,顾宇魏等.一种用于天线组阵的合成权值盲估计算法[J].计算机应用研究.2010,27(10):3747-3749.
[26]John Toon, Data from Space: Adaptive Array Network Could Improve Access to NASA's EarthObserving Satellites [EB]. http://gtresearchnews.gatech.edu/newsrelease/adaptive-array.htm, May23,2005.
[27]史学书,王元钦.深空组阵EIGEN算法及其低信噪比合成性能分析[J].宇航学报.2009,30(6):2347-2353.
[28]Jun Cheng, Huijing Dou, Qian Lei, Wenxue Li. The Research on Performances of Dual ReceiversPositioning System [C]. International Conference on Information Technology and SoftwareEngineering,2013(211):227-235
[29]Jianxin Wu, Tong Wang, Zheng Bao. Angle Estimation for Adaptive Linear Array usingPCA-GS-ML Estimator [J]. IEEE Transactions on Aerospace and Electronic Systems. Jan.2013(49):670-677
[30]Kay S, Vankayalapati N. Improvement of TDOA Position Fixing Using the Likelihood Curvature[J]. IEEE Transactions on Signal Processing, April15,2013(61):1910-1914
[31]Hara S, Anzai D, Yabu T, Kyesan Lee, Derham T, Zemek R. A Perturbation Analysis on thePerformance of TOA and TDOA Localization in Mixed LOS/NLOS Environments [J]. IEEETransactions on Communications. February2013(61):679–689
[32]Gang Wang, Youming Li, Ansari N. A Semidefinite Relaxation Method for Source LocalizationUsing TDOA and FDOA Measurements [J]. IEEE Transactions on Vehicular Technology, Feb.2013(62):853–862
[33]束锋,朱伟强,陆锦辉,韩玉兵.基于最大比合并的最大似然TDOA/FDOA联合定位方法[J].宇航学报.2010,04:1143-1148
[34]Zheng Yang, Chenshu Wu, Tao Chen, Yiyang Zhao, Wei Gong, Yunhao Liu. Detecting OutlierMeasurements Based on Graph Rigidity for Wireless Sensor Network Localization [J]. IEEETransactions on Vehicular Technology, Jan2013(62):374-383
[35]Yi Jiang, Qing Hu, Dong Kai Yang. Hybrid TOA-TDOA Positioning Algorithm in AIS [J]. AdvancedMaterials Research, January,2013(655-657):876-881
[36]Wang Yue, Liyang Rui, Wenjia Shi, Ho K C, Shang Y. Localization of an acoustic source usingsmart phones [C].2013IEEE Consumer Communications and Networking Conference (CCNC), LasVegas, NV, USA,11-14Jan.2013:761-764
[37]Ying Liu, Xu Zhang, Dan Liu, Zhi Qiang Han. Study of Location Algorithm for Wireless SensorNetworks Based on Newton Iteration [J]. Advanced Materials Research, January,2013(645):285-289
[38]Annibale P, Filos J, Naylor P A, Rabenstein R. TDOA-Based Speed of Sound Estimation forAir Temperature and Room Geometry Inference [J]. IEEE Transactions on Audio, Speech, andLanguage Processing, Feb.2013(21):234-246
[39]Das S, Saha M. Position verification in vehicular communications [C].2013FifthInternational Conference on Communication Systems and Networks (COMSNETS), Bangalore,7-10Jan2013:1-2
[40]Salvati D, Canazza S. Adaptive Time Delay Estimation Using Filter Length Constraints forSource Localization in Reverberant Acoustic Environments [J]. IEEE Signal ProcessingLetters, May2013(20):507-510
[41]Xiong Dong Ding, Guang Qian Chu. The Application of Kalman Filtering on Vessel PositioningSystem [J]. Applied Mechanics and Materials, December,2012(263-266):501-505
[42]Jeich Mar, Shin Ru Wu, Yu Ting Wang, Kuei Chou Tsai. A Three-Dimensional Position SystemUsing Digital TDE Receiver and Cylindrical Array Antenna [J]. Applied Mechanics andMaterials. January,2013(284-287):2603-2608
[43]曾雄飞,孙贵青,黄海宁.水下目标方位估计的克拉美-罗界研究[J].电子与信息学报,2013,1(35):92-98
[44]常硕.深空探测天线高精度控制方法研究[J].测控技术.2012,9(31):64-66
[45]Long Zhang, Xianwei Zhou, Jiwen Guo. Bandwidth Constraint in Intermittently Connected DeepSpace Information Networks Under Scheduled Contacts [J]. Wireless Personal Communications.February2013(68):1255-1285
[46]孔德庆,施浒立.非均匀天线组阵SUMPLE权值信噪比及信号合成性能分析[J].宇航学报.2009.30(5):1941-1946.
[47]孔德庆,施浒立.非均匀天线组阵SUMPLE合成相位补偿及信号强度估计[J].电波科学学报.2009.24(4):632-636.
[48]Tyler Hill, Josh Berk, Jeremy Straub, Jonathan Schiralli, Brian Badders, and Nicholas J.Long. Deep Space Planetary Exploration Using Commercially Available Solar ElectricPropulsion [C].44th Lunar and Planetary Science Conference. The Woodlands, Texas. Jan.2013
[49]李兆训,胡捍英.双瑞利衰落信道下双天线接收系统分集特性分析[J].西安电子科技大学学报.2011,04:160-166
[50]李兆训,曹文魁,梁波,胡捍英.双瑞利衰落信道下最大比合并性能研究[J].电路与系统学报,2012,03:88-93
[51]Taluja P S, Hughes B L. Diversity Limits of Compact Broadband Multi-Antenna Systems [J].IEEE Journal on Selected Areas in Communications, February2013(31):326-337
[52]G Mariotti, P Tortora. Experimental validation of a dual uplink multifrequency dispersivenoise calibration scheme for Deep Space tracking [J]. Electrical&Electronics Engineering.22MAR2013
[53]N S Kardashev, Y Y Kovalev, K I Kellermann. An Earth-Space Radio Interferometer with a350,000km Baseline [J]. The Radio Science Bulletin No.343, December2012:22-29
[54]Samudra E Haque, Jeremy Straub, and David Whalen. Small Satellites with Micro-Propulsionfor Communications with the Lunar South Pole Aitkens Basin [C],2013IEEE AerospaceConference, March2013:1-12
[55]王勤民,张忠培,刘明,结凤克.空间分集干扰对齐研究[J].系统工程与电子技术.2013,01:179-183
[56]殷勤业,贾曙乔,左莎琳,董柳青,尹诗媛.分布式多天线跳空收发技术(Ⅰ)[J].西安交通大学学报.2013,01:1-6
[57]李洪烈,周洪霞,刘辉传.改善系统中断速率的一种新虚拟接收天线方案[J].电讯技术.2013,01:88-92
[58]朱婕,郭业才.基于最大比合并空间分集判决反馈盲均衡算法[J].系统仿真学报,2008,11:2843-2845
[59]Singh H S, Bharti P K, Pandey G K, Meshram M K. A compact tri-band MIMO/diversity antennafor mobile handsets [C].2013IEEE International Conference on Electronics, Computing andCommunication Technologies (CONECCT), Bangalore,17-19Jan.2013:1-6
[60]S. Vongsack, C. Phongcharoenpanich, S. Kosulvit, K. Hamamoto, and T Wakabayashi.Unidirectional antenna using two-probe excited circular ring above square reflector forpolarization diversity with high isolation [J], Electromagnetics Research,2013(133):159-176.
[61]万明康,罗柏文，于宏毅等.相关噪声条件下的多天线信号合成性能分析[J].计算机工程与应用.2010,46(32):12-14.
[62]D. H. Rogstad. The SUMPLE Algorithm for Aligning Arrays of Receiving Radio Antennas:Coherence Achieved with Less Hardware and Lower Combining Loss[R]. IPN Progress Report42-162. pp:1-29. August15,2005.
[63]Shivali Goel and lemal H. Abawajy. Performance of Smart Antennas With Receive Diversityin Wireless Sensor Networks [C]. IEEE-lnternatiOl;al Conference on Signal processing,Communications and Networking Madras Instlillte o/Technology, Anna University Chel1l1GiIndia, Jan4-6,2008,:272-278.
[64]Dau-Chyrh Chang, Cheng-Nan Hu. Smart Antennas for Advanced Communication Systems [J].Proceedings of the IEEE. July2012,(100):2233–2249.
[65]Hang Wong, Kwai-Man Luk, Chi Hou Chan, Quan Xue, Kwok Kan So, Hau Wah Lai. SmallAntennas in Wireless Communications [J]. Proceedings of the IEEE. July2012,(100):2109–2121.
[66]Y.M.Madany, H.M.Elkamchouchi, A.A.Salama. Design and analysis of optimum miniaturizedconformal smart antenna system using1×8switched Butler Matrix for wireless applications
[C].2012International Conference on Microwave and Millimeter Wave Technology (ICMMT).5-8May2012,(1):1–4.
[67]Y Rama Krishna, P E S N.Krishna Prasad, P V Subbaiah and B Prabhakara Rao. A Hybrid Modelof CLMS and ACLMS Algorithms for Smart Antennas [C]. Fourth International Conference onNetworks&Communications, New York,2013:93-104
[68]M A Soliman, T E Taha, W E Swelam and A M Gomaa.3.5/5GHz dual-band8x8adaptive arrayantenna [R], Electromagnetics Research C,2013(34):85-98.
[69]Vaibhav Narula, Rajat Srivastava. Feedforward Feedback (FFFB) Method for Dereferencingthe Non Blind Algorithms in Adaptive Beamforming [J]. Communications in Computer andInformation Science,2013(296):207-214
[70]Gary K, W Wong, Xiaohua Jia. An efficient scheduling scheme for hybrid TDMA and SDMA systemswith smart antennas in WLANs [J]. Wireless Networks, February2013(19):259-271
[71]Xuezhi Yang, Wei Jiang, Vucetic B. A Random Beamforming Technique for OmnidirectionalCoverage in Multiple-Antenna Systems [J]. IEEE Transactions on Vehicular Technology, March2013(62):1420-1425
[72]M Agatonovic, Z Stankovic, I Milovanovic, N Doncov, L Sit, T Zwick and B Milovanovic.Efficient neural network approach for2D DOA estimation based on antenna array measurements[J]. Electromagnetics Research,2013(137):741-758.
[73]Kerem Kucuk, Adnan Kavak. Connectivity Analysis for Wireless Sensor Networks with AntennaArray Integrated Central Node [J]. Wireless Personal Communications. March2013:1-10
[74]M F Jamlos, M R Kamarudin, M F Malek, M Jusoh, M A Jamlos. Smart receiver system of greenhybrid antennas [J]. Microwave and Optical Technology Letters, April2013(55):800–809
[75]Jijiang Chen, Jing Zhou, Jianfeng Lu, Research on the scheme and performance of linearSA in CDMA application [C].201315th International Conference on Advanced CommunicationTechnology (ICACT), PyeongChang, Korea (South),27-30Jan.2013:107-115
[76]王鼎,王超,吴瑛.幅相误差对MUSIC算法空域谱及分辨性能影响的分析[J].通信学报,2010,(4):55-63.
[77]赵瑞锋，谈振辉，蒋海林.无线系统中的智能天线[J].电子学报.2000,28(12):77-80.
[78]漆雪梅,沈彩耀,张效义. SIMO信道中基于奇异值分解的盲信噪比估计算法[J].信号处理.2011,27(4):552-557.
[79]吴湛击,高翔.一种新的高效自适应的联合编码调制分集的多入多出系统[J].电子学报.2013,01:131-137
[80]Lehmann, F. Joint Channel Estimation and Decoding for Trellis-Coded MIMO Two-Way RelayNetworks [J]. IEEE Journal on Selected Areas in Communications,2012,11(PP):1-14
[81]Kanno, Issei; Amano, Yoshiaki; Yamaguchi, Akira; Experimental evaluation of adaptiveimpedance control for MIMO antennas in an FDD-LTE terminal [C].2013IEEE Radio and WirelessSymposium (RWS), Austin, TX, USA.20-23Jan.2013:373–375
[82]Horia Vlad Balan, Ryan Rogalin, Antonios Michaloliakos, Konstantinos Psounis, GiuseppeCaire [C]. Achieving high data rates in a distributed MIMO system. Mobicom '12Proceedingsof the18th annual international conference on Mobile computing and networking. New York,NY, USA.2012,:41-52.
[83]姜永权,刘乃安,沈民奋. MIMO系统一种新的功率分配算法及容量分析[J].电子学报.200735(9):1749-1753.
[84]漆雪梅,张效义. SIMO信道中信噪比估计算法分析与仿真[J].计算机工程.2011.
[85]Bing Chen, Xiaoping Liu, Kefu Liu, Chong Lin. Adaptive fuzzy tracking control of nonlinearMIMO systems with time-varying delays [J]. Fuzzy Sets and Systems, April,2013(217):1-21
[86]Zeng X, Li Q. Zhang Q, Qin J. Joint Beamforming and Antenna Subarray Formation for MIMOCognitive Radios [J]. IEEE Signal Processing Letters, May2013(20):479-482
[87]Ayfer Ozgur, Olivier Leveque, David Tse. Spatial Degrees of Freedom of Large DistributedMIMO Systems and Wireless Ad hoc Networks [J]. IEEE Journal on Selected Areas inCommunications, February2013(31):202-214
[88]Brady J, Behdad N, Sayeed A. Beamspace MIMO for Millimeter-Wave Communications: SystemArchitecture, Modeling, Analysis, and Measurements [J]. IEEE Transactions on Antennas andPropagation.22May2013(PP):1
[89]Kumar Rakesh. A fully integrated2×2b/g and1×2a-band MIMO WLAN SoC in45nm CMOS formulti-radio IC [C].2013IEEE International Solid-State Circuits Conference Digest ofTechnical Papers (ISSCC), San Francisco, CA, USA.17-21Feb.2013:328-329
[90]Hoydis J, ten Brink S, Debbah M. Massive MIMO in the UL/DL of Cellular Networks: How ManyAntennas Do We Need?[J]. IEEE Journal on Selected Areas in Communications, February2013(31):160-171
[91]Rusek F, Persson D, Buon Kiong Lau, Larsson E G, Marzetta T.L, Edfors O, Tufvesson F. ScalingUp MIMO: Opportunities and Challenges with Very Large Arrays [J]. IEEE Signal ProcessingMagazine, Jan.2013(30):40-60
[92]K. Tiels, P.S.C.Heuberger, J. Schoukens. Parameter reduction of MISO Wiener-Schetzenmodels using the best linear approximation [C]. Instrumentation and Measurement TechnologyConference (I2MTC).13-16May2012:2114–2118.
[93]John G.Proakis,张力军，张宗橙，郑宝玉.数字通信[M].第四版.北京：电子工业出版社.2003：629-637.
[94]Zijian Wang, Wen Chen, Jun Li. Efficient Beamforming for MIMO Relaying Broadcast ChannelWith Imperfect Channel Estimation [J]. IEEE Transactions on Vehicular Technology. Jan.2012,(61):419–426.
[95]Sang-Hyeong Kim, Zhe-Jun Jin, Yoon-Byung Chae, and Tae-Yeoul Yun. Small Internal AntennaUsing Multiband, Wideband, and High-Isolation MIMO Techniques [J]. ETRI Journal, February2013(35):51-57
[96]Ozgur A, Packard Electr, Leveque O, Tse D. Spatial Degrees of Freedom of Large DistributedMIMO Systems and Wireless Ad Hoc Networks [J]. IEEE Journal on Selected Areas inCommunications, February2013(31):202-214
[97]Alex Stéphenne, Faouzi Bellili, and Sofiène Affes. Moments-Based SNR Estimation overLinearly Modulated Wireless SIMO Channels[J]. IEEE Transactions on WirelessCommunications,2010,9(2):714-722.
[98]N.Aboutorab, W.Hardjawana, B.Vucetic. A New Iterative Doppler-Assisted ChannelEstimation Joint With Parallel ICI Cancellation for High-Mobility MIMO-OFDM Systems [J].IEEE Transactions on Vehicular Technology. May2012,(61):1577–1589.
[99]樊昌信,詹道庸,徐炳祥,吴成柯.通信原理[M].第四版.北京:国防工业出版社.2001:22-26.
[100] S.J.Dolinar. Cramer-Rao Bounds for Signal-to-Noise Ratio and Combiner WeightEstimation [R]. TDA Progress Report42-86, Jet Propulsion Laboratory Pasadena California,April-June1986,:124-130.
[101] Shen Caiyao, Yang ke, Yu Hongyi, Zhang Tianzhong. Crame-Rao Bands of SNR Estimationfor BPSK and QPSK Signals over SIMO Channels [C]. Proceedings of20102nd InternationalConference on Signal Processing Systems, Dalian China. July5-7,2010:647-650.
[102] E.R.Rodemich, V.A.Vilnrotter. Digital Combining-Weight Estimation for BroadbandSources using Maximum-Likelihood Estimations [R]. TDA Progress Report42-116, JetPropulsion Laboratory Pasadena California, February1994,:98-109.
[103] V. A. Vilnrotter, E. R. Rodemich. A Real-Time Signal Combining System for Ka-Band FeedArrays Using Maximum Likelihood Weight Estimates [R], TDA Progress Report42-100, JetPropulsion Laboratory Pasadena California, February1990,:81-99.
[104]隋丹,葛临东.一种新的基于改进PASTd的中频信号盲信噪比估计算法[J].电子与信息学报.2007,29(7):1657-1661.
[105]张海瑛,袁超伟.一种实用的通信信号信噪比盲估计方法[J].无线电通信技术.2007,33(2):49-51.
[106]彭耿,黄知涛,陆凤波,姜文利.中频通信信号信噪比的快速盲估计[J].电子与信息学报.2010,32(1):102-106.
[107]沈彩耀,于宏毅,顾宇魏,杨珂.一种用于天线组阵的合成权值盲估计算法[J].计算机应用研究.2010,27(10):3747-3749.
[108]万明康,罗柏文，于宏毅,杨珂.相关噪声条件下的多天线信号合成性能分析[J].计算机工程与应用,2010,46(32):12-14.
[109] H.H.Tan, R.Liang and P.H.Suen, Optimum Combining of Residual Carrier Array Signalsin Correlated Noises [R], TDA Progress Report42-124, Jet Propulsion Laboratory PasadenaCalifornia, February1996,:33-52.
[110]史学书,王元钦.深空组阵Eigen算法及其低信噪比合成性能分析[J].宇航学报.2009,30(6):2347-2353.
[111] Cheung K M. Eigen theory for optimal signal combining: a unified approach [R]. TDAProgress Report,1996,42(126C):1-9.
[112] Simo Doclo, Marc Moonen. Robust Time-Delay Estimation in Highly Adverse AcousticEnvironments [C]. IEEE Workshop on Applications of Signal Processing to Audio andAcoustics2001, New Paltz, New York, October2001,:59-62.
[113] C.H.Lee, V.Vilnrotter, E.Satorius, Z.Ye, D.Fort, and K.-M. Cheung, Large-Array SignalProcessing for Deep Space Applications [R]. JPL Interplanetary Network Progress Report42-150, Jep Propulsion Laborator, Pasadena, CA, August15,2002,:1-28.
[114] Charles H.Lee, Kar-Ming Cheung and Victor A.Vilnrotter. Fast Eigen-based SignalCombining Algorithms for Large Antenna Arrays [C]. IEEE Aerospace Conference2003Paper.2003,1220(2):1123-1129.
[115] Shi Xueshu; Wang Yuanqin. Performance of Deep Space Seetion Front Eigen CombiningAlgorithm in Low SNR Condition [J]. Journal of Astronautics.2009,30(6):2347-2353.
[116] B Luo, H Yu, X Zhang, Z Shen and Q Li. On Eigen-based Signal Combining Using theAutocorrelation Coefficient [J]. IET Communications, Volume6, issue18, December2012,p.3091-3097
[117] Shen Caiyao, Yang Ke, Yu Hongyi. Cramer-Rao Bounds of SNR Estimation for BPSK and QPSKSignals in Antenna Arraying[C]. In Proc. the2nd International Comference in SignalProcessing Systems (ICSPS'2010). DaLian, China,2010:647-650.
[118]沈彩耀,于宏毅.多天线PSK信号合成权值估计的CRLB研究[J].数据采集与处理.2011,25(S):106-109.
[119]沈智翔.多天线信号合成关键技术研究与实现[D].郑州:信息工程大学.2010.
[120]杨珂,王丽,李鸥,于宏毅.基于最小二乘的天线组阵合成权值及信噪比盲估计[J].电路与系统学报.2011,16(3):123-128.
[121]沈彩耀,于宏毅,顾宇巍等.一种用于天线组阵的合成权值盲估计算法[J].计算机应用研究.2010,27(10):3747-3749.
[122]张贤达，现代信号处理（第二版）[M]，清华大学出版社，北京，2003年3月，:281-282.
[123] Arobbins H, Monroe S. A stochastic approximation method [C]. Annas of MathematicalStatistics,1951,22,:400~407.
[124] Widrow B, Stearns S D. Adaptive Signal Processing [M]. New York: Prentice-Hall,1985.
[125]秦洪峰.水下多目标定位关键技术研究[D].西安:西北工业大学.2002:27-33.
[126] Kung Yao, Ralph E. Hudson, Chris W. Reed, Daching Chen, and Flavio Lorenzelli. BlindBeamforming on a Randomly Distributed Sensor Array System [J], IEEE Journal on selectedareas in communications,1998,16(8):1555-1566.
[127] Roenko, D.Kurkin, V.Lukin, I.Djurovic. Time delay estimation for noise-like widebandsignals in non-Gaussian environment [C].2012Mediterranean Conference on EmbeddedComputing (MECO).19-21June2012,:100–103.
[128] Wenhong Liu, Tianshuang Qiu, Zhiyue Lin and Richard W. McCallum. Estimation ofPropagation Velocity of Gastric Electrical Activity Using LMSTDE [C]. Engineering inMedicine and Biology27th Annual Conference. Shanghai, China, September1-4,2005,:5954-5957.
[129] AN Liansuo, WANG Peng, JIANG Genshan, LI Gengsheng, SHEN Guoqing. Time Delay Estimationfor Boiler Tube Leak Sound Propagation [J]. Proceeding of the CSEE2012,32(2):16-23.
[130] Jing wen, Ping Li, Yumei Wen, Unbiased Adaptive Time-delay Estimation in NoisyEnvironments [C], ICARCV '06.9th International Conference on Control, Automation,Robotics and Vision.5-8Dec.2006,:1-6.
[131] Ping Li, Yumei Wen, Jing Wen. Time delay estimation method for leak location of buriedwater distribution pipes [C]. Proceedings of the7th World Congress on Intelligent Controland Automation. Chongqing, China. June25-27,2008,:3052-3056.
[132] Huijuan Wu, Yumei Wen, Jin Yang, and Ping Li. Adaptive detection and location for waterpipeline leaks in lower SNR and nonstationary environments [C].2009IEEE InternationalConference on Control and Automation Christchurch, New Zealand. December9-11,2009.:1318-1323.
[133] M.Rotoloni, S. Tomasin, L.Vangelista. Maximum Likelihood Estimation of Time andCarrier Frequency Offset for DVB-T2[J]. IEEE Transactions on Broadcasting, March2012,(58):77–86.
[134] Pushpendra Singh and Pradip Sircar. Time delays and angles of arrival estimation usingknown signals [J]. Signal, Image and Video Processing.2012,(6):171-178.
[135] Daegun Oh, Myung-kyun Kwak, Jong-Wha Chong. Subspace-Based Propagation DelayEstimation Robust to Frequency Offset for Two-Way Ranging [J]. IEEE Communications Letters,February2012,(16):216–219.
[136] Jean Pierre Delmas, Yann Meurisse. On the Cramer Rao bound and maximum likelihoodin passive time delay estimation for complex signals [C].2012IEEE InternationalConference on Acoustics, Speech and Signal Processing (ICASSP),25-30March2012,:3541–3544.
[137] Jingdong Chen, Jacob Benesty and Yiteng Huang. Robust time delay estimation exploitingredundancy among multiple microphones [J], IEEE Trans. Acoustic, Speech, Signal Processing,2003,11(6):549-557.
[138] Jacob Benesty, Jingdong Chen and Yiteng Huang. Time-Delay Estimation via LinearInterpolation and Cross Correlation[J]. IEEE Trans on Speech and Audio Processing.2004,12(5):509-519.
[139] Jingdong Chen, Yiteng (Arden) Huang and Jacob Benesty. Time Delay Estimation viaMultichannel Cross-Correlation[J]. ICASSP2005:49-52.
[140] Jacob Benesty, Yiteng Huang and Jingdong Chen. Time Delay Estimation via MinimumEntropy[J].IEEE Signal Processing Letters.2007,14(3):157-160.
[141] Jacob Benesty, Yiteng Huang and Jingdong Chen. Laplace Entropy and its Applicationto Time Delay Estimation for Speech Signals[J]. ICASSP2007:113-116.
[142] R. L. Kirlin, D. F. Moore and R. F. Kubichek. Improvement of delay measurements fromsonar arrays via sequential state estimation[J]. IEEE Trans. Acoust., Speech, SignalProcessing.1981,29(3):514–519.
[143] T. Nishiura, T. Yamada, S. Nakamura, and K. Shikano. Localization of multiple soundsources based on a CSP analysis with a microphone array [C]. Proc.IEEE ICASP,2000,:1053-1055.
[144] S. M. Griebel and M. S. Brandstein. Microphone array source localization usingrealizable delay vectors [J]. in Proc. IEEE Workshop on Applications of Signal Processingto Audio and Acoustics,2001:71-74.
[145] Huang Y, Benesty J, Elko G W. Adaptive Eignvalue Decomposition Algorithm for RealtimeAcoustic Source Localization System [C].IEEE International Conference on Acoustic, Speechand Signal Processing. Seatle, WA, USA,1998,2:937-940.
[146] Huang Y, Benesty J. Adaptive Blind Channel Identification: Multi-Channel Least MeanSquare and Newton Algorithms[C]. Proceedings of the IEEE Conference on Acoustic, Speechand Signal Processing. Orlando, florida, USA,2002,2:937-940.
[147] Jingdong Chen, Yiteng Huang and Jacob Benesty. An Adaptive Blind SIMO IdentificationApproach to Joint Multichannel Time Delay Estimation [J]. ICASSP2004. pp.53-56.
[148] Yiteng (Arden) Huang, and Jacob Benesty. A Class of Frequency-Domain AdaptiveApproaches to Blind Multichannel Identification[J]. IEEE Trans on Signal Processing.2003,51(1):11-24.
[149] H.C.So, P.C.Ching and Y.T.Chan. A new algorithm for explicit adaptation of time delay[J]. IEEE Transactions on Signal Processing, July1994,(42):1816-1820.
[150] H.C.So. Fast algorithm for nonstationary delay estimation [J]. Electronics Letters,April1998,(34):722-723.
[151] H.C.So and P.C.Ching. Performance analysis of ETDGE-an efficient and unbiased TDOAestimator for source localization [J]. IEE Proceedings Radar, Sonar and Navigation,December1998,(145):325-330.
[152] H.C.So and P.C.Ching. A comparative study of five LMS-based adaptive algorithms fornonstationary delay estimation [J]. IEE Proceedings Radar, Sonar and Navigation, Feb.2001,(148):9-15.
[153] Zheng Cheng, Tjeng Thiang Tjhung. A new time delay estimator based on ETDE [J]. IEEETransactions on Signal Processing. July2003:1859-1869.
[154] S. R. Dooley and A. K. Nandi. Adaptive time delay and frequency estimation for digitalsignal synchronization in CDMA systems [J]. in Conf. Rec. Thirty-Second Asilomar Conf.Signals, Syst., Comput.1998,(2):1838-1842.
[155]罗柏文,于宏毅.采用合成方法的多路信号自适应时延联合估计[J].信号处理.2013，2(29):159-164
[156] C. Shen and H. Yu. Time-delay alignment technique for a randomly distributed sensorarray [J]. IET Communications.2011,5(8):1068-1072.
[157] Lovell B C, Williamson R C. The statistical performance of some instantaneous frequencyestimators [J]. IEEE Trans on Acoustics, Speech, Signal Processing,1992,40(7):1708-1723.
[158] Hua Jingyu, You Xiaohu. Doppler shift estimation methods in mobile communicationsystems [J]. Journal of Southeast University(English Edition), Dec.2004,(20):405-411.
[159] D.W.Tufts and C.D.Melissinos. Simple, effective computation of principal eigenvectorand their eigenvalues and application to highresolution estimation of frequencies [C].Proc. Int. Conf. Acoust., Speech, Signal Processing, Mar.1985,:951-954.
[160] D. M. Etter and D. R. Hush. A new technique for adaptive frequency estimation andtracking [J]. IEEE Trans. ASSP,1987,(35):561–564.
[161] S.R.Dooley and A.K.Nandi. Adaptive time delay and Doppler shift estimation fornarrowband signals [J]. IEE Proc. Radar, Sonar Navig. Ocotber1999,(146):243-250.
[162] Zheng Cheng and T.T.Tjhung. Explicit frequency estimation and tracking using modulatedlagrange delay filter [C].2005Asia-Pacific Conference on Communications. Perth, WesternAustralia,3-5October2005,:916-920.
[163] Z. Cheng and T. T. Tjhung. Accurate explicit frequency estimation using modulatedlagrange delay filter [C]. The2nd IEEE International Symposium on Signal Processing andInformation Technology. Marrakech, Morocco, December2002,:634–639.
[164] R. Punchalard, J. Koseeyaporn, P. Wardkein. Indirect frequency estimation based onsecond-order adaptive FIR notch filter [J], Signal Processing.2009,89(7):1428–1435.
[165] H.C.So and P.C.Ching. Adaptive algorithm for direct frequency estimation [J]. IEEProceedings-Radar, Sonar and Navigation, December2004,6(151):359-364.
[166] T.Pitschel, H.C.So and J.Zheng. New adaptive algorithm for unbiased and directestimation of sinusoidal frequency [J]. IEICE Transactions on Fundamentals of Electronics,Communications and Computer Sciences, March2008,3(E91-A):872-874.
[167] Y.Wu, H.C.So and Y.Tan. Joint time-delay and frequency estimation using parallelfactor analysis [J]. Signal Processing. August2009,(89):1667-1670.
[168] K.W.K.Lui and H.C.So. Time delay estimator based on frequency estimation approach [J].IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences.June2010,6(E93-A):1248-1250.
[169] F.K.W.Chan, H.C.So, W.H.Lau and C.F.Chan. Structured total least squares approach forefficient frequency estimation [J]. Signal Processing. April2011,4(91):1043-1047.
[170]杨珂.随机布局多天线信号合成中的多参数联合盲估计研究[D].郑州:解放军信息工程大学,2011.
[171] Nakagawa T, Matsui M, Kobayashi T, et.al. Non-data-aided wide-range frequency offsetestimator for QAM optical coherent receivers[C]. Optical Fiber Communication Conferenceand Exposition and the National Fiber Optic Engineers Conference[C],2011:1-3.
[172] Sergienko A. B, Petrov A. V. Joint blind estimation of carrier phase and frequencyoffset for QAM signals using circular harmonic decomposition [C]. IEEE InternationalConference on Acoustics, Speech and Signal Processing.2011:22-27.
[173] Xian Zhou, Xue Chen, Keping Long. Wide-Range Frequency Offset Estimation Algorithmfor Optical Coherent Systems Using Training Sequence [J]. IEEE Photonics TechnologyLetters, Jan.12012,(24):82–84.
[174] Hoon Huh, Tulino A.M., Caire, G. Network MIMO With Linear Zero-Forcing Beamforming:Large System Analysis, Impact of Channel Estimation, and Reduced-Complexity Scheduling[J]. IEEE Transactions on Information Theory. May2012,(58):2911–2934.
[175] Zhi Kun Song, Rui Zhe Yang, Li Zhang, Peng Bo Si and Yan Hua zhang. Multi-model BasedChannel Estimation for Doubly Selective Wireless Fading Channels [J]. Communications inComputer and Information Science.2012,(288):515-525.
[176] Ming Jiang, Siji Huang,Wenkun Wen. Adaptive Polar-Linear Interpolation Aided ChannelEstimation for Wireless Communication Systems [J]. IEEE Transactions on WirelessCommunications. March2012,(11):920–926.
[177] Kai-Jie Yang, Chin-Liang Wang, Yuh-Ren Tsai. Joint Channel and Doppler SpreadEstimation over Time-Varying Flat-Fading Channels [C]. In2012IEEE75th VehicularTechnology Conference (VTC Spring),6-9May2012,:1–5.
[178] Lioliou, P. Viberg, M. and Matthaiou, M. Bayesian Approach to Channel Estimation forAF MIMO Relaying Systems [J]. IEEE Journal on Selected Areas in Communications. September2012,(30):1440–1451.
[179] Abdallah, S. Psaromiligkos, I.N. Blind Channel Estimation for Amplify-and-ForwardTwo-Way Relay Networks Employing M-PSK Modulation [J]. IEEE Transactions on SignalProcessing. July2012,(60):3604–3615.
[180] Carrasco-Alvarez, R, Parra-Michel, R., Orozco-Lugo, A.G., Tugnait, J.K.Time-Varying Channel Estimation Using Two-Dimensional Channel Orthogonalization andSuperimposed Training [J]. IEEE Transactions on Signal Processing. Aug.2012,(60):4439–4443.
[181] Medbo, J. Harrysson, F. Efficiency and accuracy enhanced super resolved channelestimation [C].20126th European Conference on Antennas and Propagation (EUCAP).26-30March2012:16–20.
[182] H.C.So. Spontaneous and Explicit Estimation of Time Delays in the Absence/Presenceof Multipath Propagation [D]. Department of Electronic Engineering, The Chinese Universityof Hong Kong,1995.
[183] Haykin. S. Adaptive Filter Theory, Fourth Edition [M]. Englenwood Cliffs, NJ:Pretice-Hall,2002.
[184] Ching, P.C., Chan, Y.T. Adaptive time delay estimation with constraints [J]. IEEETransactions on Acoustics, Speech and Signal Processing. April1988,:599-602.
[185] K.Sam Shanmugan and A.M.Breipohl, Random Signals: Detection, Estimation and DataAnalysis [M], John Wiley&Sons, Inc.,1998.
[186] I.S.Gradshtegn and I.M.Ryzhik, Tables of Tntegrals, Series and Products [M], New York,Academic Press.1980,:7.
[187] Xian Zhou, Keping Long, Rui Li, Xiaolong Yang and Zhongshan Zhang. A simple andefficient frequency offset estimation algorithm for high-speed coherent optical OFDMsystems [J].2012OSA.26March2012,(20):7350-7361.
[188] H.Saeedi-Sourck, Yan Wu, J.W.M.Bergmans, S.Sadri, B.Farhang-Boroujeny.Low-Complexity Carrier Frequency Offset Estimation for Multiuser Offset QAM Filter BankMulticarrier Systems Uplink [C].2012IEEE75th Vehicular Technology Conference (VTCSpring),6-9May2012,:1–5.
[189] Bowen Luo and Hongyi Yu. The adaptive Frequency and Phase Shift Compensator [C].ISAI2010. Chengdu China,2010,(1):272-274.
[190]罗柏文，万明康，于宏毅.两种基于自适应相位补偿的FDOA估计算法[J].数据采集与处理，2012,(1):20-26.
[191]陈彩莲.随机分布多天线组阵的关键技术研究与实现[D].郑州:解放军信息工程大学,2010.
[192]张建业.多天线接收中的自适应参数估计与合成算法研究及实现[D].郑州:解放军信息工程大学,2011.
[193] D. H. Rogstad, A. Mileant, and T. T. Pham. Antenna Arraying Techniques in the DeepSpace Network [M], Hoboken, New Jersey. John Wiley2003:73-126.
[194] National Semiconductor Corp. Microphone array amplifier reduces background noise [EB].http://mobiledevdesign.com/standards_regulations/microphone-array-amplifier-national-semiconductor-0228/index.html. Feb28,2008.
[195] Kung Yao, Ealph E.Hudson, Chris W.Reed, Daching Chen, and Flavio Lorenzelli. BlindBeamforming on a Randomly Distributed Sensor Array System [J], IEEE JOURNAL ON SELECTEDAREAS IN COMMUNICATIONS, OCTOBER1998,8(16):1555-1567.
[196] John Toon. Adaptive Array Network Could Improve Access to NASA's Earth ObservingSatellites [EB]. http://eo1.gsfc.nasa.gov/new/validationreport/AdaptiveArray.html. May23,2005.
[197] S.J.Dolinar and S.Kalson. Maximum Likelihood Estimation of Signal to Noise Ratio andCombiner Weight [R]. TDA Progress Report42-85, Jet Propulsion Laboratory PasadenaCalifornia, January-March1986,:32-41.
[198]冯正权何庆华吴宝明闫庆广高丹丹.基于高阶统计量分析的生物医学信号处理应用[J].中国医学物理学杂志.2011,5:2899-2903.
[199] Hong Tao Sun, Yong Shou Dai, Fang Wang, Xing Peng. Seismic Wavelet Estimation UsingHigh-Order Statistics and Chaos-Genetic Algorithm [J]. Advanced Materials Research.January,2012(433-440):4241-4247.
[200] Valeriu Vrabie, Eric Perrin, Jean-Luc Bodnar, Kamel Mouhoubi, and Vincent Detalle.Active IR Thermography Processing Based on Higher Order Statistics for NondestructiveEvaluation [C].20th European Signal Processing Conference (EUSIPCO2012). Bucharest,Romania, August27-31,2012,:894-898.
[201] Han-Kui Chang, Jia-Chin Lin, Meng-Lin Ku. Sequential spectrum sensing based onhigher-order statistics for cognitive radios [C].2012International Conference onCommunications and Information Technology (ICCIT),26-28June2012,:413–417.
[202] F.J.Lpez-Martnez, E. Martos-Naya, J. F.Paris, U. Fernndez-Plazaola. Higher OrderStatistics of Sampled Fading Channels with Applications [J]. IEEE Transactions onVehicular Technology, Sept.2012,(61):3342–3346.
[203] Yilmaz, F. Alouini, M. On the Computation of the Higher-Order Statistics of theChannel Capacity over Generalized Fading Channels [J]. to appear in IEEE WirelessCommunications Letters.2012,:1–4.
[204] H.H.Tan, R.Liang and P.H.Suen, Optimum Combining of Residual Carrier Array Signalsin Correlated Noises [R], TDA Progress Report42-124, Jet Propulsion Laboratory PasadenaCalifornia, February1996,:33-52.
[205]蔡振浩,于宏毅.基于二阶锥规划法的混合滤波器组最优化设计[J].北京邮电大学学报.2011,34(5):124-127.
[206]蔡振浩,于宏毅.基于加权最小二乘正则化方法的混合滤波器组最优化设计[J].信号处理.2010,26(7):1110-1114.
[207]张珺,魏学业.余弦调制滤波器组的设计研究[J].北方交通大学学报.2004.6,28(3):103-109.
[208]朱婕，郭业才.基于最大比合并空间分集判决反馈盲均衡算法[J].系统仿真学报.2008,20(11):2843-2850
[209]付永明，朱江，琚瑛珏. Gardner定时同步环路参数设计及性能分析[J].通信学报.2012,(6):191-200.
[210]朱雪阳.基于Gardner算法的位定时同步研究[D].南京理工大学.2010.
[211]曾军，黄华，李东.恒模类均衡算法研究[J].通信技术.2009,42(10):60-62.
[212] Shuo Liu, Julian Cheng and Norman. Asymptotic Error Analysis of Diversity Schemes onArbitrarily Correlated Rayleigh Channels[J]. IEEE Trans on Communications.2010,58(5):1351-1355.
[213]朱婕,郭业才.基于最大比合并空间分集判决反馈盲均衡算法[J].系统仿真学报.2008,20(11):2843-2845.
[214] E.moulines, D.Slock, PDuhamel. Subspace Methods for the Blind Identification ofMultichannel FIR Filters [J].IEEE Trans. on signal processing,1995,2(43):516-526.