MIMO雷达自适应处理与波形设计研究
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摘要
多输入多输出(Multiple-input multiple-output, MIMO)雷达是近几年提出的一种新体制雷达。MIMO雷达采用多个发射天线和多个接收天线，并且各个发射天线可以发射不同的信号。按照各天线接收目标信号的相关性，MIMO雷达可以分为分布式MIMO雷达和集中式MIMO雷达。分布式MIMO雷达通过大间距的布置天线，从不同的视角观测目标，以减弱目标RCS起伏的影响，提升检测性能。集中式MIMO雷达则是相控阵雷达的扩展，但相比相控阵雷达可以获得更多的自由度和更高的分辨力，从而提高杂波、干扰抑制以及目标参数估计性能。由于集中式MIMO雷达的这些优势，将其用于空中平台结合空时自适应处理(Space-timeadaptive processing, STAP)技术检测动目标成为研究的热点。并且由于MIMO雷达在发射波形设计上更多的自由度，波形设计是MIMO雷达研究中关注的重要问题。本文的研究对象为集中式MIMO雷达，主要分析了集中式MIMO雷达杂波加有源干扰协方差矩阵的结构及其秩，研究了降维空时自适应处理算法，并且还研究了波形设计中的方向图综合与利用杂波先验信息通过波形设计来提高回波信杂比的问题，具体的内容概括如下：
1.分析了发射正交波形时，机载MIMO雷达杂波加有源干扰协方差矩阵的结构，得到杂波加有源干扰协方差矩阵秩的上界为杂波的秩与有源干扰的秩之和减去有源干扰个数。并由此得到MIMO雷达杂波加有源干扰协方差矩阵非满秩而相控阵雷达杂波加有源干扰协方差矩阵满秩时有源干扰个数的范围。当有源干扰的数目在此范围时，相控阵雷达的理论性能严重下降，而MIMO雷达在理论上仍然有足够的自由度来抑制杂波和有源干扰，从而保证有较好的性能。通过仿真实验验证了上述结论。
2.针对机载MIMO雷达干扰和杂波抑制问题，提出了一种降维空时自适应处理算法。该算法将有源干扰加噪声协方差矩阵、杂波子空间矩阵以及目标空时导向矢量结合构造降维矩阵。通过该降维矩阵可以将全维数据维数降为杂波的秩加一，从而降低了计算复杂度和估计自适应权值所需的训练样本数。与其他同类算法相比，该算法收敛速度更快，并且其理论性能可以达到全维处理的理论性能。仿真实验表明了该算法可以达到更小的信杂噪比损失，尤其在较少训练样本时，相比于其他算法优势明显。
3.机载MIMO雷达可联合利用时间自由度、发射和接收空间自由度抑制杂波，但只能利用接收空间自由度抑制有源干扰。基于此特点，提出一种机载MIMO雷达的两级空时自适应处理方法抑制杂波和干扰。第1级处理中只利用部分空间接收自由度进行干扰抑制，同时实现降维；第2级通过匹配滤波获得发射空间自由度，并联合剩余接收空间自由度和时域自由度，进行空时联合自适应处理抑制杂波。该方法通过分级处理既有效利用了MIMO雷达的发射自由度进行杂波抑制，又同时大大减低了计算量和样本需求。理论分析表明存在强干扰时，两级处理的理论性能可以逼近全维处理的最优性能，同时仿真实验表明了该算法的有效性。
4.为了获得低旁瓣的MIMO雷达发射方向图，提出一种新的最小化峰值旁瓣或积分旁瓣的MIMO雷达方向图优化算法。由于最小化峰值旁瓣或积分旁瓣的优化问题为非凸问题，该算法通过两步来得到此非凸优化问题的全局最优解。第1步通过对发射总功率的约束进行松弛，将原问题转变为一个凸优化问题，第2步则将第1步得到的解进行尺度变换使其满足发射总功率的约束，从而得到原问题的全局最优解。仿真实验表明了该算法相比于已有算法可以获得更低的峰值旁瓣或更低的积分旁瓣。
由于MIMO雷达在形成一定的发射方向图时，发射波形不再是正交波形。针对此种情况，分析了发射任意波形时机载MIMO雷达杂波加有源干扰的协方差矩阵的结构，得到MIMO雷达杂波加有源干扰协方差矩阵秩的上界为杂波的秩与有源干扰的秩之和减去有源干扰个数，发射正交波形时的结论为该结论的特例。
5.针对地基MIMO雷达检测地面目标问题，为了降低系统输出的峰值杂信比，首先提出一种基于确知杂波冲激响应的MIMO雷达发射波形和接收滤波器联合优化算法。该算法假设杂波冲激响应为确定已知的，采用交替迭代的方法来求解原问题，并可以同时降低目标输出信号的峰值旁瓣。在仿真实验中，杂波的冲激响应由实测数据估计得到。实验结果表明该方法可以获得相比于LFM(Linearfrequency modulated)信号更低的峰值杂信比，可用于检测地面慢速或静止目标。但该方法得到的系统输出的杂波波形对杂波冲激响应的扰动较为敏感，各次脉冲输出的杂波波形变化较大，影响检测动目标的性能。
随后，针对上述方法的输出杂信比对杂波冲激响应的扰动非常敏感的问题，提出一种对杂波冲激响应稳健的MIMO雷达波形优化及接收滤波器联合设计方法，来降低对于杂波冲激响应扰动的敏感性。该方法将杂信比表示为固定的杂信比分量和扰动的杂信比分量，然后将扰动杂信比分量的最大值引入目标函数，来降低输出受杂波冲激响应扰动的影响。将该优化结果用于实测数据估计得到的多次脉冲的杂波冲激响应，虽然相比于之前的方法，输出的峰值杂信比有所上升，但明显降低了杂波输出在各次脉冲之间的变化，同时相比LFM信号有较低的峰值杂信比。
Multiple-input multiple-output (MIMO) radar is a new concept of radar systemproposed recent years. MIMO radar is implemented by multiple transmit antennas andmultiple receive antennas, and individual transmit antennas can transmit differentsignals. In general, MIMO radar can be categorized into two types according to thecorrelation coefficient of target echoes between antennas: MIMO radar with widelyseparated antennas and MIMO radar with collocated antennas. The first type of MIMOradar can observe a target from different aspects to overcome the target scintillation,thus it can improve detection performance. MIMO radar with collocated antennas is ageneration of traditional phased array radar with more spatial freedom and higherresolution. This type of MIMO radar can improve the performance of clutter andjammer suppression and the accuracy of target parameters estimation. Due to thosesuperiorities, collocated MIMO radar combined with space-time adaptive processing todetect moving target becomes a hot topic in MIMO radar research. In addition, sinceMIMO radar has more degrees of freedom, waveform design is a significant topic. Thisdissertation concerns problems related to STAP technologies in MIMO radar, includingrank analysis of clutter plus jamming covariance matrix, reduced-dimension STAPalgorithm and also develops problems related to MIMO radar waveform designincluding transmit beampattern synthesis and waveform design with priori information.The mainly content of this dissertation is summarized as follows:
1. The covariance matrix structure of clutter plus jamming is analyzed forside-look airborne MIMO radar, and the upper bound on its rank is derived, whichequals the summation of clutter and jamming rank subtracting the jammer number.According to this, a certain range of jammer number is attained. If the number ofjammers is within this range，the clutter plus jamming covariance matrix is full rank forphased array radar, but rank deficiency for MIMO radar. As a result, the performance ofphased array radar deteriorates severely under this condition, meanwhile, theperformance of MIMO radar is much better, taking advantage of sufficient degrees offreedom to suppress clutter and jamming for MIMO radar. Simulational experimentsvalidate the above conclusion.
2. A new reduced-dimension space-time adaptive processing algorithm is proposedto suppress clutter and jamming for airborne MIMO radar. The jamming plus noisecovariance is utilized to construct reduced dimension transform matrix joint with target space-time steering vector and clutter subspace matrix which can be implementedoff-line. The data dimension after reduced dimension transformation equals clutter rankplus one. Thus, the computational load and sample requirement for computing adaptiveweight are reduced apparently. Compared with other algorithms, this algorithmconverges faster, and the theoretical performance can approach that of full dimensionaladaptive processing. Numerical results show that the algorithm can achieve lesssignal-to-interference plus noise ratio loss than existed algorithm, especially with lessadaptive weight training samples.
3. Airborne MIMO radar uses its temporal freedom and receiving&transmittingspatial freedom to suppress clutter, while it nulls jammers by only receiving spatialfreedom. Based on these characteristic, a novel two-stage method is presented in thispaper, which separates suppression of clutter and jammers into two sequential stages.First, jammers are nulled with partial receiving freedom, and dimension reduction isalso implemented. Second, matched-filtering is applied to the output data after jammersuppression, followed by clutter suppression using temporal and spatial freedom.Through the two-stage processing, transmitting spatial freedom can be fully utilized inclutter suppression together with reducing the computational load and samplerequirement effectively. Both theoretic analysis and simulation experiments presentthat，in the presence of strong jammers, performance of the proposal can approach tothat of the full dimension space-time adaptive processing for MIMO radar.
4. To deal with low sidelobe transmit pattern design problem for MIMO radar, anew minimum peak-sidelobe or integrated-sidelobe transmit pattern optimizationalgorithm is proposed in this paper. Due to the optimization problem of minimizingpeak-sidelobe or integrated-sidelobe is non-convex, it is solved in two steps by theproposed algorithm. A convex problem is solved in the first step, and in the second step,the global optimum of original problem is attained by scale transformation of thesolution in the first step. The simulations show that the transmit pattern have lowerpeak-sidelobe or integrated-sidelobe with this algorithm than existed algorithm.
Since the transmit waveforms are not orthogonal when the transmit beampattern isnot omnidirectional, the covariance matrix structure of clutter plus jamming is analyzedfor side-look airborne MIMO radar with arbitrary transmit waveform for this case. Theupper bound on the rank of covariance matrix of clutter plus jamming is derived, whichis up to the summation of clutter and jamming rank subtracting the number of jammers.The conclusion in the condition of transmitting orthogonal waveforms is a special caseof this conclusion.
5. In order to decrease output peak CSR (Clutter to signal satio) of ground basedMIMO radar system in detection of ground target, a MIMO radar waveform andreceived filter joint optimization algorithm is proposed based on definite clutter impulseresponse. Assuming the clutter impulse response is known and definite, the optimizationproblem is solved through alternative iteration, and the peak sidelobe of the targetoutput signal can be decreased at the same time. In the simulation, the clutter impulseresponse is estimated from real data. Our algorithm can get lower peak CSR than LFM(Linear frequency fodulated) signal. Thus, this algorithm can be used to detect slowvelocity or stationary ground target. However the output CSR with our algorithm issensitive to the perturbation of clutter impulse response, big variation occurs among theoutput CSR of multiple pulse, thus affect the detection of moving ground target.
Subsequently, a robust waveform and received filter joint optimization algorithm isproposed to decrease the sensitivity to the perturbation of clutter impulse response. Themaximum of the perturbation component of output CSR is induced into the objectivefunction in this algorithm to decrease the effect of clutter impulse response perturbation.Through applying the optimized waveform and received filter to multi-pulse clutterimpulse response estimated from real data, although the output peak CSR is higher thanthat of previous algorithm, the variation of CSR among multiple pulse decreasesapparently, and the peak output CSR is lower than that of LFM signal.
1.分析了发射正交波形时，机载MIMO雷达杂波加有源干扰协方差矩阵的结构，得到杂波加有源干扰协方差矩阵秩的上界为杂波的秩与有源干扰的秩之和减去有源干扰个数。并由此得到MIMO雷达杂波加有源干扰协方差矩阵非满秩而相控阵雷达杂波加有源干扰协方差矩阵满秩时有源干扰个数的范围。当有源干扰的数目在此范围时，相控阵雷达的理论性能严重下降，而MIMO雷达在理论上仍然有足够的自由度来抑制杂波和有源干扰，从而保证有较好的性能。通过仿真实验验证了上述结论。
2.针对机载MIMO雷达干扰和杂波抑制问题，提出了一种降维空时自适应处理算法。该算法将有源干扰加噪声协方差矩阵、杂波子空间矩阵以及目标空时导向矢量结合构造降维矩阵。通过该降维矩阵可以将全维数据维数降为杂波的秩加一，从而降低了计算复杂度和估计自适应权值所需的训练样本数。与其他同类算法相比，该算法收敛速度更快，并且其理论性能可以达到全维处理的理论性能。仿真实验表明了该算法可以达到更小的信杂噪比损失，尤其在较少训练样本时，相比于其他算法优势明显。
3.机载MIMO雷达可联合利用时间自由度、发射和接收空间自由度抑制杂波，但只能利用接收空间自由度抑制有源干扰。基于此特点，提出一种机载MIMO雷达的两级空时自适应处理方法抑制杂波和干扰。第1级处理中只利用部分空间接收自由度进行干扰抑制，同时实现降维；第2级通过匹配滤波获得发射空间自由度，并联合剩余接收空间自由度和时域自由度，进行空时联合自适应处理抑制杂波。该方法通过分级处理既有效利用了MIMO雷达的发射自由度进行杂波抑制，又同时大大减低了计算量和样本需求。理论分析表明存在强干扰时，两级处理的理论性能可以逼近全维处理的最优性能，同时仿真实验表明了该算法的有效性。
4.为了获得低旁瓣的MIMO雷达发射方向图，提出一种新的最小化峰值旁瓣或积分旁瓣的MIMO雷达方向图优化算法。由于最小化峰值旁瓣或积分旁瓣的优化问题为非凸问题，该算法通过两步来得到此非凸优化问题的全局最优解。第1步通过对发射总功率的约束进行松弛，将原问题转变为一个凸优化问题，第2步则将第1步得到的解进行尺度变换使其满足发射总功率的约束，从而得到原问题的全局最优解。仿真实验表明了该算法相比于已有算法可以获得更低的峰值旁瓣或更低的积分旁瓣。
由于MIMO雷达在形成一定的发射方向图时，发射波形不再是正交波形。针对此种情况，分析了发射任意波形时机载MIMO雷达杂波加有源干扰的协方差矩阵的结构，得到MIMO雷达杂波加有源干扰协方差矩阵秩的上界为杂波的秩与有源干扰的秩之和减去有源干扰个数，发射正交波形时的结论为该结论的特例。
5.针对地基MIMO雷达检测地面目标问题，为了降低系统输出的峰值杂信比，首先提出一种基于确知杂波冲激响应的MIMO雷达发射波形和接收滤波器联合优化算法。该算法假设杂波冲激响应为确定已知的，采用交替迭代的方法来求解原问题，并可以同时降低目标输出信号的峰值旁瓣。在仿真实验中，杂波的冲激响应由实测数据估计得到。实验结果表明该方法可以获得相比于LFM(Linearfrequency modulated)信号更低的峰值杂信比，可用于检测地面慢速或静止目标。但该方法得到的系统输出的杂波波形对杂波冲激响应的扰动较为敏感，各次脉冲输出的杂波波形变化较大，影响检测动目标的性能。
随后，针对上述方法的输出杂信比对杂波冲激响应的扰动非常敏感的问题，提出一种对杂波冲激响应稳健的MIMO雷达波形优化及接收滤波器联合设计方法，来降低对于杂波冲激响应扰动的敏感性。该方法将杂信比表示为固定的杂信比分量和扰动的杂信比分量，然后将扰动杂信比分量的最大值引入目标函数，来降低输出受杂波冲激响应扰动的影响。将该优化结果用于实测数据估计得到的多次脉冲的杂波冲激响应，虽然相比于之前的方法，输出的峰值杂信比有所上升，但明显降低了杂波输出在各次脉冲之间的变化，同时相比LFM信号有较低的峰值杂信比。
Multiple-input multiple-output (MIMO) radar is a new concept of radar systemproposed recent years. MIMO radar is implemented by multiple transmit antennas andmultiple receive antennas, and individual transmit antennas can transmit differentsignals. In general, MIMO radar can be categorized into two types according to thecorrelation coefficient of target echoes between antennas: MIMO radar with widelyseparated antennas and MIMO radar with collocated antennas. The first type of MIMOradar can observe a target from different aspects to overcome the target scintillation,thus it can improve detection performance. MIMO radar with collocated antennas is ageneration of traditional phased array radar with more spatial freedom and higherresolution. This type of MIMO radar can improve the performance of clutter andjammer suppression and the accuracy of target parameters estimation. Due to thosesuperiorities, collocated MIMO radar combined with space-time adaptive processing todetect moving target becomes a hot topic in MIMO radar research. In addition, sinceMIMO radar has more degrees of freedom, waveform design is a significant topic. Thisdissertation concerns problems related to STAP technologies in MIMO radar, includingrank analysis of clutter plus jamming covariance matrix, reduced-dimension STAPalgorithm and also develops problems related to MIMO radar waveform designincluding transmit beampattern synthesis and waveform design with priori information.The mainly content of this dissertation is summarized as follows:
1. The covariance matrix structure of clutter plus jamming is analyzed forside-look airborne MIMO radar, and the upper bound on its rank is derived, whichequals the summation of clutter and jamming rank subtracting the jammer number.According to this, a certain range of jammer number is attained. If the number ofjammers is within this range，the clutter plus jamming covariance matrix is full rank forphased array radar, but rank deficiency for MIMO radar. As a result, the performance ofphased array radar deteriorates severely under this condition, meanwhile, theperformance of MIMO radar is much better, taking advantage of sufficient degrees offreedom to suppress clutter and jamming for MIMO radar. Simulational experimentsvalidate the above conclusion.
2. A new reduced-dimension space-time adaptive processing algorithm is proposedto suppress clutter and jamming for airborne MIMO radar. The jamming plus noisecovariance is utilized to construct reduced dimension transform matrix joint with target space-time steering vector and clutter subspace matrix which can be implementedoff-line. The data dimension after reduced dimension transformation equals clutter rankplus one. Thus, the computational load and sample requirement for computing adaptiveweight are reduced apparently. Compared with other algorithms, this algorithmconverges faster, and the theoretical performance can approach that of full dimensionaladaptive processing. Numerical results show that the algorithm can achieve lesssignal-to-interference plus noise ratio loss than existed algorithm, especially with lessadaptive weight training samples.
3. Airborne MIMO radar uses its temporal freedom and receiving&transmittingspatial freedom to suppress clutter, while it nulls jammers by only receiving spatialfreedom. Based on these characteristic, a novel two-stage method is presented in thispaper, which separates suppression of clutter and jammers into two sequential stages.First, jammers are nulled with partial receiving freedom, and dimension reduction isalso implemented. Second, matched-filtering is applied to the output data after jammersuppression, followed by clutter suppression using temporal and spatial freedom.Through the two-stage processing, transmitting spatial freedom can be fully utilized inclutter suppression together with reducing the computational load and samplerequirement effectively. Both theoretic analysis and simulation experiments presentthat，in the presence of strong jammers, performance of the proposal can approach tothat of the full dimension space-time adaptive processing for MIMO radar.
4. To deal with low sidelobe transmit pattern design problem for MIMO radar, anew minimum peak-sidelobe or integrated-sidelobe transmit pattern optimizationalgorithm is proposed in this paper. Due to the optimization problem of minimizingpeak-sidelobe or integrated-sidelobe is non-convex, it is solved in two steps by theproposed algorithm. A convex problem is solved in the first step, and in the second step,the global optimum of original problem is attained by scale transformation of thesolution in the first step. The simulations show that the transmit pattern have lowerpeak-sidelobe or integrated-sidelobe with this algorithm than existed algorithm.
Since the transmit waveforms are not orthogonal when the transmit beampattern isnot omnidirectional, the covariance matrix structure of clutter plus jamming is analyzedfor side-look airborne MIMO radar with arbitrary transmit waveform for this case. Theupper bound on the rank of covariance matrix of clutter plus jamming is derived, whichis up to the summation of clutter and jamming rank subtracting the number of jammers.The conclusion in the condition of transmitting orthogonal waveforms is a special caseof this conclusion.
5. In order to decrease output peak CSR (Clutter to signal satio) of ground basedMIMO radar system in detection of ground target, a MIMO radar waveform andreceived filter joint optimization algorithm is proposed based on definite clutter impulseresponse. Assuming the clutter impulse response is known and definite, the optimizationproblem is solved through alternative iteration, and the peak sidelobe of the targetoutput signal can be decreased at the same time. In the simulation, the clutter impulseresponse is estimated from real data. Our algorithm can get lower peak CSR than LFM(Linear frequency fodulated) signal. Thus, this algorithm can be used to detect slowvelocity or stationary ground target. However the output CSR with our algorithm issensitive to the perturbation of clutter impulse response, big variation occurs among theoutput CSR of multiple pulse, thus affect the detection of moving ground target.
Subsequently, a robust waveform and received filter joint optimization algorithm isproposed to decrease the sensitivity to the perturbation of clutter impulse response. Themaximum of the perturbation component of output CSR is induced into the objectivefunction in this algorithm to decrease the effect of clutter impulse response perturbation.Through applying the optimized waveform and received filter to multi-pulse clutterimpulse response estimated from real data, although the output peak CSR is higher thanthat of previous algorithm, the variation of CSR among multiple pulse decreasesapparently, and the peak output CSR is lower than that of LFM signal.
引文
[1]丁鹭飞，耿富录.雷达原理.西安:西安电子科技大学出版社,2006.
[2] Skolnik M I. Introduction to radar systems.3rd ed. New York: McGraw-Hill,2001.
[3] Skolnik M I. Fifty years of radar. Proceedings of the IEEE,1985,73(2):182-197.
[4]张光义.相控阵雷达系统.北京:国防工业出版社,1994.
[5] Fowler C A. Old Radar Types Never Die; They just phased array or55years oftrying to avoid mechanical scan. IEEE Aerospace and Electronic SystemsMagazine,1998,13(9):24A-24L.
[6] Fenn A J, Temme D H, Delaney W P, et al. The development of phased-arrayradar technology. Lincoln Laboratory Journal,2000,12(2):321-340.
[7]张光义,赵玉洁.相控阵雷达技术.北京:电子工业出版社,2006.
[8] B lcskei H, Gesbert D, Papadias C B, et al. Space-time wireless system: Fromarray processing to MIMO communications. New York: Cambridge UniversityPress,2006.
[9] Bliss D W, Forsythe K W. Multiple-input multiple-output (MIMO) radar andimaging: degrees of freedom and resolution. Proceedings of the ConferenceRecord of the Thirty-Seventh Asilomar Conference on Signals, Systems andComputers,2003:54-59.
[10] Rabideau D J, Parker P. Ubiquitous MIMO multifunction digital array radar.Proceedings of the Thirty-Seventh Asilomar Conference on Signals, Systems andComputers,2003:1057-1064.
[11] Fishler E, Haimovich A, Blum R, et al. MIMO radar: an idea whose time hascome. Proceedings of the IEEE Radar Conference, Philadelphia,2004:71-78.
[12] Fishler E, Haimovich A, Blum R, et al. Performance of MIMO radar systems:advantages of angular diversity. Proceedings of the Conference Record of theThirty-Eighth Asilomar Conference on Signals, Systems and Computers,2004:305-309.
[13] Forsythe K W, Bliss D W, Fawcett G S. Multiple-input multiple-output (MIMO)radar: performance issues. Proceedings of the Thirty-Eighth Asilomar Conferenceon Signals, Systems and Computers,2004:310-315.
[14] Fishler E, Haimovich A, Blum R S, et al. Spatial diversity in radars-models anddetection performance. IEEE Transactions on Signal Processing,2006,54(3):823-838.
[15] Haimovich A M, Blum R S, Cimini L J. MIMO radar with widely separatedantennas. IEEE Signal Processing Magazine,2008,25(1):116-129.
[16] Jian L, Stoica P. MIMO radar with colocated antennas. IEEE Signal ProcessingMagazine,2007,24(5):106-114.
[17]杨振起,张永顺,骆永军.双(多)基地雷达系统.北京:国防工业出版社,2001.
[18]何友,修建娟,张晶炜.雷达数据处理及应用.2nd ed.北京:电子工业出版社,2009.
[19] Stimson G W. Introduction to airborne radar.2nd ed. Mendham, N.J.: SciTech,1998.
[20] Ward J, Space-time adaptive processing for airborne radar.Lexington, MA,: MITLincoln Lab.,1015.1994.
[21] Klemm R. Principles of Space-time adaptive processing.3rd ed. London: TheInstitution of Engineering and Technology,2006.
[22] Guerci J R. Space-time adaptive processing for radar. Artech House Publishers,2003.
[23] Griffiths H D, Vinagre L, Lee W K. Developments in radar waveform design.Proceedings of the12th International Conference on Microwaves and Radar,1998:56-76.
[24] Rummler W D. Clutter suppression by complex weighting of coherent pulsetrains. IEEE Transactions on Aerospace and Electronic Systems,1966,2(6):689-699.
[25] Delong D, Hofstetter E. On the design of optimum radar waveforms for clutterrejection. IEEE Transactions on Information Theory,1967,13(3):454-463.
[26] Rummler W D. A technique for improving the clutter performance of coherentpulse train signals. IEEE Transactions on Aerospace and Electronic Systems,1967,3(6):898-906.
[27] Mitchell R L, Rihaczek A W. Clutter suppression properties of weighted pulsetrains. IEEE Transactions on Aerospace and Electronic Systems,1968,4(6):822-828.
[28] Mosca E. A nonadaptive processing of radar pulse trains for clutter rejection.IEEE Transactions on Aerospace and Electronic Systems,1969,5(6):951-958.
[29] Dorey J, Garnier G, Auvray G. RIAS, synthetic impulse and antenna radar.Proceedings of the International Conference on Radar, Paris,1989:556-562.
[30] Luce A S, Molina H, Muller D, et al. Experimental results on RIAS digitalbeamforming radar. Proceedings of the Radar92International Conference,1992:74-77.
[31] Colin J M. Phased array radars in France: present and future. Proceedings of theIEEE International Symposium onPhased Array Systems and Technology,1996:458-462.
[32]张庆文,保铮,张玉洪.一种在接收端综合发射阵列波束的新方法.现代雷达,1992,14(3):41-51.
[33]张庆文,保铮,张玉洪.稀布阵综合脉冲和孔径雷达的接收信号处理.现代雷达,1992,14(5):32-42.
[34]张庆文,保铮,张玉洪.综合脉冲和天线雷达的时空三维匹配滤波及其性能分析.电子科学学刊,1994,16(5):481-489.
[35]保铮，张庆文.一种新型的米波雷达──综合脉冲与孔径雷达.现代雷达,1995,17(1):1-13.
[36] Sheikhi A, Zamani A. Adaptive target detection in clutter using MIMO radar.Proceedings of the InternationalRadar Symposium,2006:1-4.
[37] Sheikhi A, Zamani A, Norouzi Y. Model-based adaptive target detection inclutter using MIMO radar. Proceedings of the CIE International Conference onRadar, Shanghai, China,2006: I:57-60.
[38] Zhou S H, Liu H W. Signal fusion-based target detection algorithm for spatialdiversity radar. IET Radar, Sonar&Navigation,2011,5(3):204-214.
[39] Aittomaki T, Koivunen V. Performance of MIMO radar with angular diversityunder swerling scattering models. IEEE Journal of Selected Topics in SignalProcessing,2010,4(1):101-114.
[40] Du C, Thompson J S, Petillot Y. Predicted detection performance of MIMO radar.IEEE Signal Processing Letters,2008,15:83-86.
[41] Akcakaya M, Nehorai A. MIMO radar detection and adaptive design under aphase synchronization mismatch. IEEE Transactions on Signal Processing,2010,58(10):4994-5005.
[42] Goodman N A, Bruyere D. Optimum and decentralized detection for multistaticairborne radar. IEEE Transactions on Aerospace and Electronic Systems,2007,43(2):806-813.
[43] Tang J, Li N, Wu Y, et al. On detection performance of MIMO radar a relativeentropy-based study. IEEE Signal Processing Letters,2009,16(3):184-187.
[44] De Maio A, Lops M. Design principles of MIMO radar detectors. IEEETransactions on Aerospace and Electronic Systems,2007,43(3):886-898.
[45] Sammartino P F, Baker C J, Griffiths H D. Adaptive MIMO radar system inclutter. Proceedings of the IEEE Radar Conference,2007:276-281.
[46] Akcakaya M, Nehorai A. MIMO radar detection and adaptive design incompound-Gaussian clutter. Proceedings of the IEEE Radar Conference,2010:236-241.
[47] Chong C Y, Pascal F, Ovarlez J P, et al. MIMO radar detection in Non-Gaussianand heterogeneous clutter. IEEE Journal of Selected Topics in Signal Processing,2010,4(1):115-126.
[48] Cui G, Lingjiang K, Xiaobo Y, et al. Two-step GLRT design of MIMO radar incompound-Gaussian clutter. Proceedings of the IEEE Radar Conference,2010:343-347.
[49] Kong L, Cui G, Yang X, et al. Rao and wald tests design of polarimetricmultiple-input multiple-output radar in compound-gaussian clutter. IET SignalProcessing,2011,5(1):85-96.
[50] Godrich H, Haimovich A M, Blum R S. Cramer Rao bound on target localizationestimation in MIMO radar systems. Proceedings of the42nd Annual Conferenceon Information Sciences and Systems,2008:134-139.
[51] Gogineni S, Nehorai A. Target tracking using monopulse MIMO radar withdistributed antennas. Proceedings of the IEEE Radar Conference,2010:194-199.
[52] He Q, Blum R S. Cramer-Rao Bound for MIMO radar target localization withphase errors. IEEE Signal Processing Letters,2010,17(1):83-86.
[53] He Q, Blum R S, Godrich H, et al. Target velocity estimation and antennaplacement for MIMO radar with widely separated antennas. IEEE Journal ofSelected Topics in Signal Processing,2010,4(1):79-100.
[54] Chiriac V M, Haimovich A M. Ziv-Zakai lower bound on target localizationestimation in MIMO radar systems. Proceedings of the IEEE Radar Conference,2010:678-683.
[55] Xia W, He Z. Multiple-target localization and estimation of MIMO radars withunknown transmitted signals. Proceedings of the ISCAS IEEE InternationalSymposium on Circuits and Systems,2008:3009-3012.
[56] Gogineni S, Nehorai A. Target tracking using monopulse MIMO radar withdistributed antennas. Proceedings of the IEEE Radar Conference,2010:194-199.
[57] Xu L, Li J. Iterative generalized-likelihood ratio test for MIMO radar. IEEETransactions on Signal Processing,2007,55(6):2375-2385.
[58] Jin M, Liao G, Li J. Target localisation for distributed multiple-inputmultiple-output radar and its performance analysis. IET Radar, Sonar&Navigation,2011,5(1):83-91.
[59] Schmidt R O. Multiple emitter location and signal parameter estimation. IEEETransactions on Antenna Propagation,1986,34(3):276-280.
[60] Robey F C, Coutts S, Weikle D, et al. MIMO radar theory and experimentalresults. Proceedings of the Thirty-Eighth Asilomar Conference on Signals,Systems and Computers,2004:300-304.
[61] Li J, Stoica P, Xu L, et al. On Parameter identifiability of MIMO radar. IEEESignal Processing Letters,2007,14(12):968-971.
[62] Xu L, Li J, Stoica P. Target detection and parameter estimation for MIMO radarsystems. IEEE Transactions on Aerospace and Electronic Systems,2008,44(3):927-939.
[63] Capon J. High-resolution frequency-wavenumber spectrum analysis. Proceedingsof the IEEE,1969,57(8):1408-1418.
[64] Li J, Stoica P. An adaptive filtering approach to spectral estimation and SARimaging. IEEE Transactions on Signal Processing,1996,44(6):1469-1484.
[65] Jakobsson A, Stoica P. Combining Capon and APES for estimation of spectrallines. Circuits, Systems, and Signal Processing,2000,19(2):159-169.
[66] Xu L, Stoica P, Li J. A diagonal growth curve model and some signal-processingapplications. IEEE Transactions on Signal Processing,2006,54(9):3363-3371.
[67] Li J, Stoica P, Wang Z. On robust Capon beamforming and diagonal loading.IEEE Transactions on Signal Processing,2003,51(7):1702-1715.
[68] Stoica P, Zhisong W, Jian L. Robust Capon beamforming. IEEE SignalProcessing Letters,2003,10(6):172-175.
[69] Li J, Stoica P, Wang Z. Doubly constrained robust Capon beamformer. IEEETransactions on Signal Processing,2004,52(9):2407-2423.
[70] Li J, Stoica P, Zheng X. Signal synthesis and receiver design for MIMO radarimaging. IEEE Transactions on Signal Processing,2008,56(8):3959-3968.
[71] Roberts W, Stoica P, Li J, et al. Iterative adaptive approaches to MIMO radarimaging. IEEE Journal of Selected Topics in Signal Processing,2010,4(1):5-20.
[72] Yardibi T, Li J, Stoica P, et al. Source localization and sensing: a nonparametriciterative adaptive approach based on weighted least squares. IEEE Transactionson Aerospace and Electronic Systems,2010,46(1):425-443.
[73] Tan X, Roberts W, Li J, et al. Sparse learning via iterative minimization withapplication to MIMO radar imaging. IEEE Transactions on Signal Processing,2011,59(3):1088-1101.
[74]井伟,武其松,邢孟道,等.多子带并发的MIMO-SAR高分辨大测绘带成像.系统仿真学报,2008,20(16):4373-4378.
[75]王力宝,许稼,皇甫堪,等. MIMO-SAR等效相位中心误差分析与补偿.电子学报,2009,37(12):2688-2693.
[76]武其松,井伟,邢孟道,等. MIMO-SAR大测绘带成像.电子与信息学报,2009,31(4):772-775.
[77]段广青,王党卫,马晓岩. MIMO雷达三维成像方法研究.空军雷达学院学报,2009,23(3):171-174.
[78] Wang D-W, Ma X-Y, Su Y. Two-dimensional imaging via a narrowband MIMOradar system with two perpendicular linear arrays. IEEE Transactions on ImageProcessing,2010,19(5):1269-1279.
[79] Wang D-W, Ma X-Y, Chen A L, et al. High-resolution imaging using a widebandMIMO radar system with two distributed arrays. IEEE Transactions on ImageProcessing,2010,19(5):1280-1289.
[80]王怀军. MIMO雷达成像算法研究.国防科学技术大学,2010.
[81] Duofang C, Baixiao C, Guodong Q. Angle estimation using ESPRIT in MIMOradar. Electronics Letters,2008,44(12):770-771.
[82] Yan H, Li J, Liao G. Multitarget identification and localization using bistaticMIMO radar systems. EURASIP J Adv Signal Process,2008:1-8.
[83] Jin M, Liao G, Li J. Joint DOD and DOA estimation for bistatic MIMO radar.Signal Processing,2009,89(2):244-251.
[84]陈金立,顾红,苏卫民.一种双基地MIMO雷达快速多目标定位方法.电子与信息学报,2009,31(7):1664-1668.
[85] Bencheikh M L, Wang Y. Joint DOD-DOA estimation using combinedESPRIT-MUSIC approach in MIMO radar. Electronics Letters,2010,46(15):1081-1083.
[86] Chen J, Gu H, Su W. A new method for joint DOD and DOA estimation inbistatic MIMO radar. Signal Processing,2010,90(2):714-718.
[87] Li J, Liao G, Ma K, et al. Waveform decorrelation for multitarget localization inbistatic MIMO radar systems. Proceedings of the IEEE Radar Conference,2010:21-24.
[88] Liu X L, Liao G S. Multi-target localisation in bistatic MIMO radar. ElectronicsLetters,2010,46(13):945-946.
[89] Yunhe C. Joint estimation of angle and Doppler frequency for bistatic MIMOradar. Electronics Letters,2010,46(2):170-172.
[90] Dang B, Li J, Liao G. Taylor polynomial expansion based waveform correlationcancellation for bistatic MIMO radar localization. Signal Processing,2012,92(6):1404-1410.
[91]程院兵，顾红，苏卫民.基于联合矩阵对角化的双基地MIMO雷达DOD-DOA估计.电子与信息学报,2012,34(4):904-909.
[92] Guimei Z, Baixiao C, Minglei Y. Unitary ESPRIT algorithm for bistatic MIMOradar. Electronics Letters,2012,48(3):179-181.
[93] Liu X, Liao G. Direction finding and mutual coupling estimation for bistaticMIMO radar. Signal Processing,2012,92(2):517-522.
[94] Xie R, Liu Z, Wu J-X. Direction finding with automatic pairing for bistaticMIMO radar. Signal Processing,2012,92(1):198-203.
[95] Zheng Z, Zhang J, Zhang J. Joint DOD and DOA estimation of bistatic MIMOradar in the presence of unknown mutual coupling. Signal Processing,2012,92(12):3039-3048.
[96]王克让，何劲，贺亚鹏，等.基于矢量传感器的扩展孔径双基地MIMO雷达多目标定位算法.电子与信息学报,2012,34(3):582-586.
[97] Zeng J, He Z, Liu B. Adaptive space-time-waveform processing for MIMO radar.Proceedings of the International Conference on Communications, Circuits andSystems,2007:641-643.
[98] Kantor J, Davis S K. Airborne GMTI using MIMO techniques. Proceedings ofthe IEEE Radar Conference,2010:1344-1349.
[99] Wang G, Lu Y, Sun J. STAP performance analysis for MIMO radar withwaveform diversity. Proceedings of the IEEE Radar Conference,2009:1-6.
[100] Wu Y, Tang J, Peng Y. Models and performance evaluation for multiple-inputmultiple-output space-time adaptive processing radar. IET Radar, Sonar&Navigation,2009,3(6):569-582.
[101] Bliss D W, Forsythe K W, Davis S K, et al. GMTI MIMO radar. Proceedings ofthe International Waveform Diversity and Design Conference,2009:118-122.
[102] Zatman M. The applicability of GMTI MIMO Radar. Proceedings of theConference Record of the Forty Fourth Asilomar Conference on Signals, Systemsand Computers,2010:2138-2142.
[103] Forsythe K W, Bliss D W. MIMO radar waveform constraints for GMTI. IEEEJournal of Selected Topics in Signal Processing,2010,4(1):21-32.
[104] Chen C-Y, Vaidyanathan P P. MIMO Radar space-time adaptive processingusing prolate spheroidal wave functions. IEEE Transactions on Signal Processing2008,56(2):623-635.
[105] Brennan L E, Staudaher F M, Subclutter visibility demonstration. TechnicalReport RL-TR-92-21, Adaptive Sensors Incorporated.1992.
[106] Wang G, Lu Y. Clutter Rank of STAP in MIMO radar with waveform diversity.IEEE Transactions on Signal Processing,2010,58(2):938-943.
[107] Zhang W, Li J, Lin H, et al. Estimation of clutter rank of MIMO radar in case ofsubarraying. Electronics Letters,2011,47(11):671-673.
[108] Li J, Liao G, Griffiths H. Bistatic MIMO radar space-time adaptive processing.Proceedings of the IEEE Radar Conference,2011:498-502.
[109] Rui F, De Lamare R C. Knowledge-aided reduced-rank STAP for MIMO radarbased on joint iterative constrained optimization of adaptive filters with multipleconstraints. Proceedings of the IEEE International Conference onAcousticsSpeech and Signal Processing,2010:2762-2765.
[110] Xiang C, Feng D-Z, Lv H, et al. Three-dimensional reduced-dimensiontransformation for MIMO radar space–time adaptive processing. SignalProcessing,2011,91(8):2121-2126.
[111]吕晖，冯大政，和洁，等.机载MIMO雷达两级降维杂波抑制方法.电子与信息学报,2011,33(4):805-809.
[112]李彩彩，廖桂生，朱圣棋，等. MIMO雷达子阵级m-Capon方法研究.系统工程与电子技术,2010,32(6):1117-1120.
[113] Mecca V F, Ramakrishnan D, Krolik J L. MIMO radar space-time adaptiveprocessing for multipath clutter mitigation. Proceedings of the IEEE WorkshopSensor Array and Multichannel Signal Processing,2006:249-253.
[114] Mecca V F, Krolik J L. Slow-time MIMO STAP with improved power efficiency.Proceedings of the Forty-First Asilomar Conference on Signals, Systems andComputers,2007:202-206.
[115] Deng H. Polyphase code design for orthogonal netted radar systems. IEEETransactions on Signal Processing,2004,52(11):3126-3135.
[116] Deng H. Discrete frequency-coding waveform design for netted radar systems.IEEE Signal Processing Letters,2004,11(2):179-182.
[117] Liu B, He Z, Zeng J, et al. Polyphase orthogonal code design for MIMO radarsystems. Proceedings of the International Conference on Radar,2006:1-4.
[118] Liu B, He Z, He Q. Optimization of orthogonal discrete frequency-codingwaveform based on modified genetic algorithm for MIMO radar. Proceedings ofthe International Conference on Communications, Circuits and Systems,2007:966-970.
[119] Khan H A, Edwards D J. Doppler problems in orthogonal MIMO radars.Proceedings of the IEEE Conference on Radar,2006:1-4.
[120] Zhang Y, Wang J. Improved design of DFCW for MIMO radar. ElectronicsLetters,2009,45(5):285-286.
[121] Fuhrmann D R, San Antonio G S. Transmit beamforming for MIMO radarsystems using signal cross-correlation. IEEE Transactions on Aerospace andElectronic Systems,2008,44(1):171-186.
[122] San Antonio G, Fuhrmann D R. Beampattern synthesis for wideband MIMOradar systems. Proceedings of the1st IEEE International Workshop onComputational Advances in Multi-Sensor Adaptive Processing,2005:105-108.
[123] Stoica P, Jian L, Yao X. On Probing signal design for MIMO radar. IEEETransactions on Signal Processing,2007,55(8):4151-4161.
[124] Li J, Xu L, Stoica P, et al. Range compression and waveform optimization forMIMO Radar: a Cramer–Rao Bound based study. IEEE Transactions on SignalProcessing,2008,56(1):218-232.
[125] Ahmed S, Thompson J S, Petillot Y R, et al. Unconstrained synthesis ofcovariance matrix for MIMO radar transmit beampattern. IEEE Transactions onSignal Processing,2011,59(8):3837-3849.
[126]胡亮兵，刘宏伟，杨晓超,等.集中式MIMO雷达发射方向图快速设计方法.电子与信息学报,2010,32(2):481-484.
[127] He H, Stoica P, Li J. Designing Unimodular sequence sets with goodcorrelations-including an application to MIMO radar. IEEE Transactions onSignal Processing,2009,57(11):4391-4405.
[128] Ahmed S, Thompson J S, Petillot Y R, et al. Finite alphabet constant-envelopewaveform design for MIMO radar. IEEE Transactions on Signal Processing,2011,59(11):5326-5337.
[129] Yang Y, Blum R S. MIMO radar waveform design based on mutual informationand minimum mean-square error estimation. IEEE Transactions on Aerospaceand Electronic Systems,2007,43(1):330-343.
[130] Yang Y, Blum R S. Minimax robust MIMO radar waveform design. IEEEJournal of Selected Topics in Signal Processing,2007,1(1):147-155.
[131] Yang Y, Blum R S, Zishu H, et al. MIMO radar waveform design via alternatingprojection. IEEE Transactions on Signal Processing,2010,58(3):1440-1445.
[132] Tang B, Tang J, Peng Y. MIMO radar waveform design in colored noise basedon information theory. IEEE Transactions on Signal Processing,2010,58(9):4684-4697.
[133] Tang B, Tang J, Peng Y. Waveform optimization for MIMO radar in colorednoise: further results for estimation-oriented criteria. IEEE Transactions on SignalProcessing,2012,60(3):1517-1522.
[134] Naghibi T, Behnia F. MIMO radar waveform design in the presence of clutter.IEEE Transactions on Aerospace and Electronic Systems,2011,47(2):770-781.
[135] Naghibi T, Namvar M, Behnia F. Optimal and robust waveform design forMIMO radars in the presence of clutter. Signal Processing,2010,90(4):1103-1117.
[136] Pillai S U, Oh H S, Youla D C, et al. Optimal transmit-receiver design in thepresence of signal-dependent interference and channel noise. IEEE Transactionson Information Theory,2000,46(2):577-584.
[137] Friedlander B. Waveform design for MIMO radars. IEEE Transactions onAerospace and Electronic Systems,2007,43(3):1227-1238.
[138] Chen C-Y, Vaidyanathan P P. MIMO radar waveform optimization with priorinformation of the extended target and clutter. IEEE Transactions on SignalProcessing,2009,57(9):3533-3544.
[1] Brennan L E, Reed L S. Theory of adaptive radar. IEEE Transactions onAerospace and Electronic Systems,1973,9(2):237-252.
[2] Reed I S, Mallett J D, Brennan L E. Rapid convergence rate in adaptive arrays.IEEE Transactions on Aerospace and Electronic Systems,1974,10(6):853-863.
[3] Klemm R. Optimum clutter suppression in airborne phased array radars.Proceedings of the Acoustics, Speech, and Signal Processing, IEEE InternationalConference on ICASSP '82,1982:1509-1512.
[4] Klemm R. Suboptimum clutter suppressing for airborne phased array radar.Proceedings of the IEE International Conference on Radar, London, UK,1982:473-476.
[5] Klemm R. Adaptive clutter suppression for airborne phased array radars.Communications, Radar and Signal Processing, IEE Proceedings F,1983,130(1):125-132.
[6] Klemm R. Adaptive airborne MTI: an auxiliary channel approach.Communications, Radar and Signal Processing, IEE Proceedings F,1987,134(3):269-276.
[7] Klemm R, Ender J. New aspects of airborne MTI. Proceedings of the IEEE1990InternationalRadar Conference,1990:335-340.
[8] Klemm R. Adaptive airborne MTI with two-dimensional motion compensation.Radar and Signal Processing, IEE Proceedings F,1991,138(6):551-558.
[9] Klemm R. Adaptive air-and spaceborne MTI under jamming conditions.Proceedings of the IEEE NationalRadar Conference,1993:167-172.
[10] Klemm R. Adaptive airborne MTI: comparison of sideways and forward lookingradar. Proceedings of the IEEE InternationalRadar Conference,1995:614-618.
[11] Klemm R. Real-time adaptive airborne MTI. I. Space-time processing.Proceedings of the CIE International Conference of Radar,1996:755-760.
[12] Klemm R. Real-time adaptive airborne MTI. II. Space-frequency processing.Proceedings of the CIE International Conference of Radar,1996:430-433.
[13] Klemm R. Prospectives in STAP research. Proceedings of the IEEE Sensor Arrayand Multichannel Signal Processing Workshop,2000:7-11.
[14] Klemm R. Applications of space-time adaptive processing. IEE Press,2002.
[15] Klemm R. Principles of Space-Time Adaptive Processing.3rd ed. London: TheInstitution of Engineering and Technology,2006.
[16] Bao Z, Liao G S, Wu R B, et al. Adaptive spatial-temporal processing forairborne radars.. Chinese Journal of Electronics,1993,2(1):2-7.
[17]保铮,廖桂生,吴仁彪,等.相控阵机载雷达杂波抑制的时空维自适应滤波.电子学报,1993,21(9):1-7.
[18] Bao Z, Wu S, Liao G, et al. Review of reduced rank space-time adaptiveprocessing for airborne radar. Proceedings of the CIE International Conference ofRadar,1996:766-769.
[19] Dipietro R C. Extended factored space-time processing for airborne radar systems.Proceedings of the The Twenty-Sixth Asilomar Conference on Signals, Systemsand Computers,1992:425-430.
[20] Wang H, Cai L. On adaptive spatial-temporal processing for airborne surveillanceradar systems. IEEE Transactions on Aerospace and Electronic Systems,1994,30(3):660-670.
[21] Brown R D, Wicks M C, Zhang Y, et al. A space-time adaptive processingapproach for improved performance and affordability. Proceedings of the IEEENational Radar Conference,1996:321-326.
[22] Zhang Y, Wang H. Further results of ΣΔ-STAP approach to airborne surveillanceradars. Proceedings of the IEEE NationalRadar Conference,1997:337-342.
[23]王永良,吴志文,彭应宁.适于非均匀环境的空时自适应处理方法.电子学报,1999,27(9):63-66.
[24] Ward J, Space-time adaptive processing for airborne radar.Lexington, MA,: MITLincoln Lab.,1015.1994,December.
[25] Haimovich A M, Bar-Ness Y. An eigenanalysis interference canceler. IEEETransactions on Signal Processing,1991,39(1):76-84.
[26] Haimovich A M, Berin M. Eigenanalysis-based space-time adaptive radar:performance analysis. IEEE Transactions on Aerospace and Electronic Systems,1997,33(4):1170-1179.
[27] Haimovich A. The eigencanceler: adaptive radar by eigenanalysis methods. IEEETransactions on Aerospace and Electronic Systems,1996,32(2):532-542.
[28] Goldstein J S, Reed I S. Reduced-rank adaptive filtering. IEEE Transactions onSignal Processing,1997,45(2):492-496.
[29] Goldstein J S, Reed I S. Subspace selection for partially adaptive sensor arrayprocessing. IEEE Transactions on Aerospace and Electronic Systems,1997,33(2):539-544.
[30] Goldstein J S, Reed I S. Theory of partially adaptive radar. IEEE Transactions onAerospace and Electronic Systems,1997,33(4):1309-1325.
[31] Goldstein J S, Reed I S, Scharf L L. A multistage representation of the Wienerfilter based on orthogonal projections. IEEE Transactions on Information Theory,1998,44(7):2943-2959.
[32] Brennan L E, Staudaher F M. Subclutter visibility demonstration. AdaptiveSensors Incorporated.1992.
[33] Chen C-Y, Vaidyanathan P P. MIMO radar space-time adaptive processing usingprolate spheroidal wave functions. IEEE Transactions on Signal Processing,2008,56(2):623-635.
[1] Brennan L E and Reed I S. Theory of adaptive radar. IEEE Transactions onAerospace and Electronic Systems,1973,9(2):237-252.
[2] Ward J. Space-time adaptive processing for airborne radar. MIT Lincoln Lab.,Lexington, MA, Technical Report.1015,1994.
[3] Klemm R. Principles of Space-Time Adaptive Processing.3rd Edition. London.The Institution of Engineering and Technology,2006:59-60.
[4] Richardson P G. STAP covariance matrix structure and its impact on clutter plusjamming suppression solutions. Electronics Letters,2001,37(2):118-119.
[5] Chen C-Y, Vaidyanathan P P. MIMO Radar SpaceTime Adaptive ProcessingUsing Prolate Spheroidal Wave Functions. IEEE Transactions on SignalProcessing,2008,56(2):623-635.
[6] Skolnik M I. Radar Handbook.2nd Edition. Boston, Massachusetts.McGraw-Hill,1990.
[1] Bliss D W, Forsythe K W. Multiple-input multiple-output (MIMO) radar andimaging: degrees of freedom and resolution. Proceedings of the ConferenceRecord of the Thirty-Seventh Asilomar Conference on Signals, Systems andComputers,2003:54-59.
[2] Mecca V F, Ramakrishnan D, Krolik J L. MIMO Radar Space-Time AdaptiveProcessing for Multipath Clutter Mitigation. Proceedings of the IEEE WorkshopSensor Array and Multichannel Signal Processing,2006:249-253.
[3] Wu Y, Tang J, Peng Y. Models and performance evaluation for multiple-inputmultiple-output space-time adaptive processing radar. IET Radar, Sonar&Navigation,2009,3(6):569–582.
[4]张西川,谢文冲,张永顺,等.任意波形相关性的机载MIMO雷达杂波建模与分析.电子与信息学报,2011,(3):646-651.
[5] Wang G, Lu Y. Clutter Rank of STAP in MIMO Radar With WaveformDiversity. IEEE Transactions on Signal Processing,2010,58(2):938-943.
[6] Chen C-Y, Vaidyanathan P P. MIMO Radar SpaceTime Adaptive ProcessingUsing Prolate Spheroidal Wave Functions. IEEE Transactions on SignalProcessing,2008,56(2):623-635.
[7]李彩彩,廖桂生,朱圣棋,等. MIMO雷达子阵级m-Capon方法研究.系统工程与电子技术,2010,(6):1117-1120.
[8]吕晖,冯大政,和洁,等.机载MIMO雷达两级降维杂波抑制方法.电子与信息学报,2011,(4):805-809.
[9] Ward J, Space-Time Adaptive Processing for Airborne Radar. Lexington, MA:MIT Lincoln Lab.,1015.1994, December.
[10]吕晖,冯大政,和洁,等.一种简化的机载MIMO雷达杂波特征相消器.航空学报,2011,(5):866-872.
[11] Reed I S, Mallett J D, Brennan L E. Rapid convergence rate in adaptive arrays.IEEE Transactions on Aerospace and Electonic Systems,1974,10(6):853-863.
[12] Klemm R. Principles of space-time adaptive processing.3rd ed. London: TheInstitution of Engineering and Technology,2006.
[13] Bao Z, Wu S, Liao G, et al. Review of reduced rank space-time adaptiveprocessing for airborne radar. Proceedings of the CIE International Conference ofRadar,1996:766-769.
[14] Dipietro R C. Extended factored space-time processing for airborne radar systems.Proceedings of the The Twenty-Sixth Asilomar Conference on Signals, Systemsand Computers,1992:425-430.
[15] Horn R A, Johnson C R. Matrix Analysis. Cambridge University Press,1990.
[16]吕晖.集中式MIMO雷达信号处理方法研究.西安电子科技大学,2011.
[1] Stoica P, Jian L, Yao X. On probing signal design for MIMO Radar. IEEETransactions on Signal Processing,2007,55(8):4151-4161.
[2] Tree H L V. Optimum array processing. New York: John Wiley&Sons,2002.
[3] Fuhrmann D R, San Antonio G S. Transmit beamforming for MIMO radarsystems using signal cross-correlation. IEEE Transactions on Aerospace andElectronic Systems,2008,44(1):171-186.
[4] Hassanien A, Vorobyov S A. Transmit energy focusing for DOA estimation inMIMO radar with colocated antennas. IEEE Transactions on Signal Processing,2011,59(6):2669-2682.
[5] Ahmed S, Thompson J S, Petillot Y R, et al. Unconstrained synthesis ofcovariance matrix for MIMO radar transmit beampattern. IEEE Transactions onSignal Processing,2011,59(8):3837-3849.
[6] Shadi K, Behnia F. Transmit beampattern synthesis using eigenvaluedecomposition in MIMO radar.Proceedings of the8th International Conferenceon Information, Communications and Signal Processing (ICICS),2011:1-5.
[7] Li J, Xu L, Stoica P, et al. Range compression and waveform optimization forMIMO Radar: a Cramer–Rao Bound based study. IEEE Transactions on SignalProcessing,2008,56(1):218-232.
[8] Skolnik I M. Radar handbook.2nd ed. Boston, Massachusetts: McGraw-Hill,1990.
[9] Wang G, Lu Y. Clutter Rank of STAP in MIMO radar with waveform diversity.IEEE Transactions on Signal Processing,2010,58(2):938-943.
[10]张西川,谢文冲,张永顺,等.任意波形相关性的机载MIMO雷达杂波建模与分析.电子与信息学报,2011,(3):646-651.
[11] Sturm J F. Using SeDuMi1.02, A Matlab toolbox for optimization oversymmetric cones. Optimization Methods and Software,1999,11(1-4):625-653.
[12] Grant M, Boyd S. CVX: Matlab software for disciplined convex programming.http://stanford.edu/~boyd/cvx.2008.
[13] Ward J. Space-time adaptive processing for airborne radar. MIT Lincoln Lab.,Lexington, MA, Technical Report.1015,1994.
[14] Klemm R. Principles of Space-Time Adaptive Processing.3rd Edition. London.The Institution of Engineering and Technology,2006:59-60.
[15] Richardson P G. STAP covariance matrix structure and its impact on clutter plusjamming suppression solutions. Electronics Letters,2001,37(2):118-119.
[16]张贤达.矩阵分析与应用.北京:清华大学出版社,2004.
[17] Wang G, Lu Y. Clutter Rank of STAP in MIMO radar with waveform diversity.IEEE Transactions on Signal Processing,2010,58(2):938-943.
[1] Rummler W D. Clutter suppression by complex weighting of coherent pulsetrains. IEEE Transactions on Aerospace and Electronic Systems,1966,2(6):689-699.
[2] Delong D, Hofstetter E. On the design of optimum radar waveforms for clutterrejection. IEEE Transactions on Information Theory,1967,13(3):454-463.
[3] Rummler W D. A technique for improving the clutter performance of coherentpulse train signals. IEEE Transactions on Aerospace and Electronic Systems,1967,3(6):898-906.
[4] Mitchell R L, Rihaczek A W. Clutter suppression properties of weighted pulsetrains. IEEE Transactions on Aerospace and Electronic Systems,1968,4(6):822-828.
[5] Mosca E. A nonadaptive processing of radar pulse trains for clutter rejection.IEEE Transactions on Aerospace and Electronic Systems,1969,5(6):951-958.
[6] Kay S. Optimal signal design for detection of gaussian point targets in stationarygaussian clutter/reverberation. IEEE Journal of Selected Topics in SignalProcessing,2007,1(1):31-41.
[7] Pillai S U, Oh H S, Youla D C, et al. Optimal transmit-receiver design in thepresence of signal-dependent interference and channel noise. IEEE Transactionson Information Theory,2000,46(2):577-584.
[8] Garren D A, Osborn M K, Odom A C, et al. Enhanced target detection andidentification via optimised radar transmission pulse shape. IEE Proceedings ofRadar, Sonar and Navigation,2001,148(3):130-138.
[9]纠博,刘宏伟,李丽亚,等.基于相位调制的宽带雷达波形优化方法.电子与信息学报,2008,30(9):2038-2041.
[10] Jiu B, Liu H, Feng D, et al. Minimax robust transmission waveform and receivingfilter design for extended target detection with imprecise prior knowledge. SignalProcessing,2012,92(1):210-218.
[11] Friedlander B. Waveform design for MIMO radars. IEEE Transactions onAerospace and Electronic Systems,2007,43(3):1227-1238.
[12] Chen C-Y, Vaidyanathan P P. MIMO radar waveform optimization with priorinformation of the extended target and clutter. IEEE Transactions on SignalProcessing,2009,57(9):3533-3544.
[13]庄珊娜,贺亚鹏,朱晓华.用于扩展目标检测的OFDM-MIMO雷达波形设计.南京理工大学学报,2012,(2):309-313.
[14] Naghibi T, Namvar M, Behnia F. Optimal and robust waveform design forMIMO radars in the presence of clutter. Signal Processing,2010,90(4):1103-1117.
[15] Grant M, and Boyd S. CVX: Matlab software for disciplined convexprogramming,2008.
[16] Magnus J R and Neudecker H. The commutation matrix: Some properties andapplacations. The Annals of Statistics,7(2):381-394.
[2] Skolnik M I. Introduction to radar systems.3rd ed. New York: McGraw-Hill,2001.
[3] Skolnik M I. Fifty years of radar. Proceedings of the IEEE,1985,73(2):182-197.
[4]张光义.相控阵雷达系统.北京:国防工业出版社,1994.
[5] Fowler C A. Old Radar Types Never Die; They just phased array or55years oftrying to avoid mechanical scan. IEEE Aerospace and Electronic SystemsMagazine,1998,13(9):24A-24L.
[6] Fenn A J, Temme D H, Delaney W P, et al. The development of phased-arrayradar technology. Lincoln Laboratory Journal,2000,12(2):321-340.
[7]张光义,赵玉洁.相控阵雷达技术.北京:电子工业出版社,2006.
[8] B lcskei H, Gesbert D, Papadias C B, et al. Space-time wireless system: Fromarray processing to MIMO communications. New York: Cambridge UniversityPress,2006.
[9] Bliss D W, Forsythe K W. Multiple-input multiple-output (MIMO) radar andimaging: degrees of freedom and resolution. Proceedings of the ConferenceRecord of the Thirty-Seventh Asilomar Conference on Signals, Systems andComputers,2003:54-59.
[10] Rabideau D J, Parker P. Ubiquitous MIMO multifunction digital array radar.Proceedings of the Thirty-Seventh Asilomar Conference on Signals, Systems andComputers,2003:1057-1064.
[11] Fishler E, Haimovich A, Blum R, et al. MIMO radar: an idea whose time hascome. Proceedings of the IEEE Radar Conference, Philadelphia,2004:71-78.
[12] Fishler E, Haimovich A, Blum R, et al. Performance of MIMO radar systems:advantages of angular diversity. Proceedings of the Conference Record of theThirty-Eighth Asilomar Conference on Signals, Systems and Computers,2004:305-309.
[13] Forsythe K W, Bliss D W, Fawcett G S. Multiple-input multiple-output (MIMO)radar: performance issues. Proceedings of the Thirty-Eighth Asilomar Conferenceon Signals, Systems and Computers,2004:310-315.
[14] Fishler E, Haimovich A, Blum R S, et al. Spatial diversity in radars-models anddetection performance. IEEE Transactions on Signal Processing,2006,54(3):823-838.
[15] Haimovich A M, Blum R S, Cimini L J. MIMO radar with widely separatedantennas. IEEE Signal Processing Magazine,2008,25(1):116-129.
[16] Jian L, Stoica P. MIMO radar with colocated antennas. IEEE Signal ProcessingMagazine,2007,24(5):106-114.
[17]杨振起,张永顺,骆永军.双(多)基地雷达系统.北京:国防工业出版社,2001.
[18]何友,修建娟,张晶炜.雷达数据处理及应用.2nd ed.北京:电子工业出版社,2009.
[19] Stimson G W. Introduction to airborne radar.2nd ed. Mendham, N.J.: SciTech,1998.
[20] Ward J, Space-time adaptive processing for airborne radar.Lexington, MA,: MITLincoln Lab.,1015.1994.
[21] Klemm R. Principles of Space-time adaptive processing.3rd ed. London: TheInstitution of Engineering and Technology,2006.
[22] Guerci J R. Space-time adaptive processing for radar. Artech House Publishers,2003.
[23] Griffiths H D, Vinagre L, Lee W K. Developments in radar waveform design.Proceedings of the12th International Conference on Microwaves and Radar,1998:56-76.
[24] Rummler W D. Clutter suppression by complex weighting of coherent pulsetrains. IEEE Transactions on Aerospace and Electronic Systems,1966,2(6):689-699.
[25] Delong D, Hofstetter E. On the design of optimum radar waveforms for clutterrejection. IEEE Transactions on Information Theory,1967,13(3):454-463.
[26] Rummler W D. A technique for improving the clutter performance of coherentpulse train signals. IEEE Transactions on Aerospace and Electronic Systems,1967,3(6):898-906.
[27] Mitchell R L, Rihaczek A W. Clutter suppression properties of weighted pulsetrains. IEEE Transactions on Aerospace and Electronic Systems,1968,4(6):822-828.
[28] Mosca E. A nonadaptive processing of radar pulse trains for clutter rejection.IEEE Transactions on Aerospace and Electronic Systems,1969,5(6):951-958.
[29] Dorey J, Garnier G, Auvray G. RIAS, synthetic impulse and antenna radar.Proceedings of the International Conference on Radar, Paris,1989:556-562.
[30] Luce A S, Molina H, Muller D, et al. Experimental results on RIAS digitalbeamforming radar. Proceedings of the Radar92International Conference,1992:74-77.
[31] Colin J M. Phased array radars in France: present and future. Proceedings of theIEEE International Symposium onPhased Array Systems and Technology,1996:458-462.
[32]张庆文,保铮,张玉洪.一种在接收端综合发射阵列波束的新方法.现代雷达,1992,14(3):41-51.
[33]张庆文,保铮,张玉洪.稀布阵综合脉冲和孔径雷达的接收信号处理.现代雷达,1992,14(5):32-42.
[34]张庆文,保铮,张玉洪.综合脉冲和天线雷达的时空三维匹配滤波及其性能分析.电子科学学刊,1994,16(5):481-489.
[35]保铮，张庆文.一种新型的米波雷达──综合脉冲与孔径雷达.现代雷达,1995,17(1):1-13.
[36] Sheikhi A, Zamani A. Adaptive target detection in clutter using MIMO radar.Proceedings of the InternationalRadar Symposium,2006:1-4.
[37] Sheikhi A, Zamani A, Norouzi Y. Model-based adaptive target detection inclutter using MIMO radar. Proceedings of the CIE International Conference onRadar, Shanghai, China,2006: I:57-60.
[38] Zhou S H, Liu H W. Signal fusion-based target detection algorithm for spatialdiversity radar. IET Radar, Sonar&Navigation,2011,5(3):204-214.
[39] Aittomaki T, Koivunen V. Performance of MIMO radar with angular diversityunder swerling scattering models. IEEE Journal of Selected Topics in SignalProcessing,2010,4(1):101-114.
[40] Du C, Thompson J S, Petillot Y. Predicted detection performance of MIMO radar.IEEE Signal Processing Letters,2008,15:83-86.
[41] Akcakaya M, Nehorai A. MIMO radar detection and adaptive design under aphase synchronization mismatch. IEEE Transactions on Signal Processing,2010,58(10):4994-5005.
[42] Goodman N A, Bruyere D. Optimum and decentralized detection for multistaticairborne radar. IEEE Transactions on Aerospace and Electronic Systems,2007,43(2):806-813.
[43] Tang J, Li N, Wu Y, et al. On detection performance of MIMO radar a relativeentropy-based study. IEEE Signal Processing Letters,2009,16(3):184-187.
[44] De Maio A, Lops M. Design principles of MIMO radar detectors. IEEETransactions on Aerospace and Electronic Systems,2007,43(3):886-898.
[45] Sammartino P F, Baker C J, Griffiths H D. Adaptive MIMO radar system inclutter. Proceedings of the IEEE Radar Conference,2007:276-281.
[46] Akcakaya M, Nehorai A. MIMO radar detection and adaptive design incompound-Gaussian clutter. Proceedings of the IEEE Radar Conference,2010:236-241.
[47] Chong C Y, Pascal F, Ovarlez J P, et al. MIMO radar detection in Non-Gaussianand heterogeneous clutter. IEEE Journal of Selected Topics in Signal Processing,2010,4(1):115-126.
[48] Cui G, Lingjiang K, Xiaobo Y, et al. Two-step GLRT design of MIMO radar incompound-Gaussian clutter. Proceedings of the IEEE Radar Conference,2010:343-347.
[49] Kong L, Cui G, Yang X, et al. Rao and wald tests design of polarimetricmultiple-input multiple-output radar in compound-gaussian clutter. IET SignalProcessing,2011,5(1):85-96.
[50] Godrich H, Haimovich A M, Blum R S. Cramer Rao bound on target localizationestimation in MIMO radar systems. Proceedings of the42nd Annual Conferenceon Information Sciences and Systems,2008:134-139.
[51] Gogineni S, Nehorai A. Target tracking using monopulse MIMO radar withdistributed antennas. Proceedings of the IEEE Radar Conference,2010:194-199.
[52] He Q, Blum R S. Cramer-Rao Bound for MIMO radar target localization withphase errors. IEEE Signal Processing Letters,2010,17(1):83-86.
[53] He Q, Blum R S, Godrich H, et al. Target velocity estimation and antennaplacement for MIMO radar with widely separated antennas. IEEE Journal ofSelected Topics in Signal Processing,2010,4(1):79-100.
[54] Chiriac V M, Haimovich A M. Ziv-Zakai lower bound on target localizationestimation in MIMO radar systems. Proceedings of the IEEE Radar Conference,2010:678-683.
[55] Xia W, He Z. Multiple-target localization and estimation of MIMO radars withunknown transmitted signals. Proceedings of the ISCAS IEEE InternationalSymposium on Circuits and Systems,2008:3009-3012.
[56] Gogineni S, Nehorai A. Target tracking using monopulse MIMO radar withdistributed antennas. Proceedings of the IEEE Radar Conference,2010:194-199.
[57] Xu L, Li J. Iterative generalized-likelihood ratio test for MIMO radar. IEEETransactions on Signal Processing,2007,55(6):2375-2385.
[58] Jin M, Liao G, Li J. Target localisation for distributed multiple-inputmultiple-output radar and its performance analysis. IET Radar, Sonar&Navigation,2011,5(1):83-91.
[59] Schmidt R O. Multiple emitter location and signal parameter estimation. IEEETransactions on Antenna Propagation,1986,34(3):276-280.
[60] Robey F C, Coutts S, Weikle D, et al. MIMO radar theory and experimentalresults. Proceedings of the Thirty-Eighth Asilomar Conference on Signals,Systems and Computers,2004:300-304.
[61] Li J, Stoica P, Xu L, et al. On Parameter identifiability of MIMO radar. IEEESignal Processing Letters,2007,14(12):968-971.
[62] Xu L, Li J, Stoica P. Target detection and parameter estimation for MIMO radarsystems. IEEE Transactions on Aerospace and Electronic Systems,2008,44(3):927-939.
[63] Capon J. High-resolution frequency-wavenumber spectrum analysis. Proceedingsof the IEEE,1969,57(8):1408-1418.
[64] Li J, Stoica P. An adaptive filtering approach to spectral estimation and SARimaging. IEEE Transactions on Signal Processing,1996,44(6):1469-1484.
[65] Jakobsson A, Stoica P. Combining Capon and APES for estimation of spectrallines. Circuits, Systems, and Signal Processing,2000,19(2):159-169.
[66] Xu L, Stoica P, Li J. A diagonal growth curve model and some signal-processingapplications. IEEE Transactions on Signal Processing,2006,54(9):3363-3371.
[67] Li J, Stoica P, Wang Z. On robust Capon beamforming and diagonal loading.IEEE Transactions on Signal Processing,2003,51(7):1702-1715.
[68] Stoica P, Zhisong W, Jian L. Robust Capon beamforming. IEEE SignalProcessing Letters,2003,10(6):172-175.
[69] Li J, Stoica P, Wang Z. Doubly constrained robust Capon beamformer. IEEETransactions on Signal Processing,2004,52(9):2407-2423.
[70] Li J, Stoica P, Zheng X. Signal synthesis and receiver design for MIMO radarimaging. IEEE Transactions on Signal Processing,2008,56(8):3959-3968.
[71] Roberts W, Stoica P, Li J, et al. Iterative adaptive approaches to MIMO radarimaging. IEEE Journal of Selected Topics in Signal Processing,2010,4(1):5-20.
[72] Yardibi T, Li J, Stoica P, et al. Source localization and sensing: a nonparametriciterative adaptive approach based on weighted least squares. IEEE Transactionson Aerospace and Electronic Systems,2010,46(1):425-443.
[73] Tan X, Roberts W, Li J, et al. Sparse learning via iterative minimization withapplication to MIMO radar imaging. IEEE Transactions on Signal Processing,2011,59(3):1088-1101.
[74]井伟,武其松,邢孟道,等.多子带并发的MIMO-SAR高分辨大测绘带成像.系统仿真学报,2008,20(16):4373-4378.
[75]王力宝,许稼,皇甫堪,等. MIMO-SAR等效相位中心误差分析与补偿.电子学报,2009,37(12):2688-2693.
[76]武其松,井伟,邢孟道,等. MIMO-SAR大测绘带成像.电子与信息学报,2009,31(4):772-775.
[77]段广青,王党卫,马晓岩. MIMO雷达三维成像方法研究.空军雷达学院学报,2009,23(3):171-174.
[78] Wang D-W, Ma X-Y, Su Y. Two-dimensional imaging via a narrowband MIMOradar system with two perpendicular linear arrays. IEEE Transactions on ImageProcessing,2010,19(5):1269-1279.
[79] Wang D-W, Ma X-Y, Chen A L, et al. High-resolution imaging using a widebandMIMO radar system with two distributed arrays. IEEE Transactions on ImageProcessing,2010,19(5):1280-1289.
[80]王怀军. MIMO雷达成像算法研究.国防科学技术大学,2010.
[81] Duofang C, Baixiao C, Guodong Q. Angle estimation using ESPRIT in MIMOradar. Electronics Letters,2008,44(12):770-771.
[82] Yan H, Li J, Liao G. Multitarget identification and localization using bistaticMIMO radar systems. EURASIP J Adv Signal Process,2008:1-8.
[83] Jin M, Liao G, Li J. Joint DOD and DOA estimation for bistatic MIMO radar.Signal Processing,2009,89(2):244-251.
[84]陈金立,顾红,苏卫民.一种双基地MIMO雷达快速多目标定位方法.电子与信息学报,2009,31(7):1664-1668.
[85] Bencheikh M L, Wang Y. Joint DOD-DOA estimation using combinedESPRIT-MUSIC approach in MIMO radar. Electronics Letters,2010,46(15):1081-1083.
[86] Chen J, Gu H, Su W. A new method for joint DOD and DOA estimation inbistatic MIMO radar. Signal Processing,2010,90(2):714-718.
[87] Li J, Liao G, Ma K, et al. Waveform decorrelation for multitarget localization inbistatic MIMO radar systems. Proceedings of the IEEE Radar Conference,2010:21-24.
[88] Liu X L, Liao G S. Multi-target localisation in bistatic MIMO radar. ElectronicsLetters,2010,46(13):945-946.
[89] Yunhe C. Joint estimation of angle and Doppler frequency for bistatic MIMOradar. Electronics Letters,2010,46(2):170-172.
[90] Dang B, Li J, Liao G. Taylor polynomial expansion based waveform correlationcancellation for bistatic MIMO radar localization. Signal Processing,2012,92(6):1404-1410.
[91]程院兵，顾红，苏卫民.基于联合矩阵对角化的双基地MIMO雷达DOD-DOA估计.电子与信息学报,2012,34(4):904-909.
[92] Guimei Z, Baixiao C, Minglei Y. Unitary ESPRIT algorithm for bistatic MIMOradar. Electronics Letters,2012,48(3):179-181.
[93] Liu X, Liao G. Direction finding and mutual coupling estimation for bistaticMIMO radar. Signal Processing,2012,92(2):517-522.
[94] Xie R, Liu Z, Wu J-X. Direction finding with automatic pairing for bistaticMIMO radar. Signal Processing,2012,92(1):198-203.
[95] Zheng Z, Zhang J, Zhang J. Joint DOD and DOA estimation of bistatic MIMOradar in the presence of unknown mutual coupling. Signal Processing,2012,92(12):3039-3048.
[96]王克让，何劲，贺亚鹏，等.基于矢量传感器的扩展孔径双基地MIMO雷达多目标定位算法.电子与信息学报,2012,34(3):582-586.
[97] Zeng J, He Z, Liu B. Adaptive space-time-waveform processing for MIMO radar.Proceedings of the International Conference on Communications, Circuits andSystems,2007:641-643.
[98] Kantor J, Davis S K. Airborne GMTI using MIMO techniques. Proceedings ofthe IEEE Radar Conference,2010:1344-1349.
[99] Wang G, Lu Y, Sun J. STAP performance analysis for MIMO radar withwaveform diversity. Proceedings of the IEEE Radar Conference,2009:1-6.
[100] Wu Y, Tang J, Peng Y. Models and performance evaluation for multiple-inputmultiple-output space-time adaptive processing radar. IET Radar, Sonar&Navigation,2009,3(6):569-582.
[101] Bliss D W, Forsythe K W, Davis S K, et al. GMTI MIMO radar. Proceedings ofthe International Waveform Diversity and Design Conference,2009:118-122.
[102] Zatman M. The applicability of GMTI MIMO Radar. Proceedings of theConference Record of the Forty Fourth Asilomar Conference on Signals, Systemsand Computers,2010:2138-2142.
[103] Forsythe K W, Bliss D W. MIMO radar waveform constraints for GMTI. IEEEJournal of Selected Topics in Signal Processing,2010,4(1):21-32.
[104] Chen C-Y, Vaidyanathan P P. MIMO Radar space-time adaptive processingusing prolate spheroidal wave functions. IEEE Transactions on Signal Processing2008,56(2):623-635.
[105] Brennan L E, Staudaher F M, Subclutter visibility demonstration. TechnicalReport RL-TR-92-21, Adaptive Sensors Incorporated.1992.
[106] Wang G, Lu Y. Clutter Rank of STAP in MIMO radar with waveform diversity.IEEE Transactions on Signal Processing,2010,58(2):938-943.
[107] Zhang W, Li J, Lin H, et al. Estimation of clutter rank of MIMO radar in case ofsubarraying. Electronics Letters,2011,47(11):671-673.
[108] Li J, Liao G, Griffiths H. Bistatic MIMO radar space-time adaptive processing.Proceedings of the IEEE Radar Conference,2011:498-502.
[109] Rui F, De Lamare R C. Knowledge-aided reduced-rank STAP for MIMO radarbased on joint iterative constrained optimization of adaptive filters with multipleconstraints. Proceedings of the IEEE International Conference onAcousticsSpeech and Signal Processing,2010:2762-2765.
[110] Xiang C, Feng D-Z, Lv H, et al. Three-dimensional reduced-dimensiontransformation for MIMO radar space–time adaptive processing. SignalProcessing,2011,91(8):2121-2126.
[111]吕晖，冯大政，和洁，等.机载MIMO雷达两级降维杂波抑制方法.电子与信息学报,2011,33(4):805-809.
[112]李彩彩，廖桂生，朱圣棋，等. MIMO雷达子阵级m-Capon方法研究.系统工程与电子技术,2010,32(6):1117-1120.
[113] Mecca V F, Ramakrishnan D, Krolik J L. MIMO radar space-time adaptiveprocessing for multipath clutter mitigation. Proceedings of the IEEE WorkshopSensor Array and Multichannel Signal Processing,2006:249-253.
[114] Mecca V F, Krolik J L. Slow-time MIMO STAP with improved power efficiency.Proceedings of the Forty-First Asilomar Conference on Signals, Systems andComputers,2007:202-206.
[115] Deng H. Polyphase code design for orthogonal netted radar systems. IEEETransactions on Signal Processing,2004,52(11):3126-3135.
[116] Deng H. Discrete frequency-coding waveform design for netted radar systems.IEEE Signal Processing Letters,2004,11(2):179-182.
[117] Liu B, He Z, Zeng J, et al. Polyphase orthogonal code design for MIMO radarsystems. Proceedings of the International Conference on Radar,2006:1-4.
[118] Liu B, He Z, He Q. Optimization of orthogonal discrete frequency-codingwaveform based on modified genetic algorithm for MIMO radar. Proceedings ofthe International Conference on Communications, Circuits and Systems,2007:966-970.
[119] Khan H A, Edwards D J. Doppler problems in orthogonal MIMO radars.Proceedings of the IEEE Conference on Radar,2006:1-4.
[120] Zhang Y, Wang J. Improved design of DFCW for MIMO radar. ElectronicsLetters,2009,45(5):285-286.
[121] Fuhrmann D R, San Antonio G S. Transmit beamforming for MIMO radarsystems using signal cross-correlation. IEEE Transactions on Aerospace andElectronic Systems,2008,44(1):171-186.
[122] San Antonio G, Fuhrmann D R. Beampattern synthesis for wideband MIMOradar systems. Proceedings of the1st IEEE International Workshop onComputational Advances in Multi-Sensor Adaptive Processing,2005:105-108.
[123] Stoica P, Jian L, Yao X. On Probing signal design for MIMO radar. IEEETransactions on Signal Processing,2007,55(8):4151-4161.
[124] Li J, Xu L, Stoica P, et al. Range compression and waveform optimization forMIMO Radar: a Cramer–Rao Bound based study. IEEE Transactions on SignalProcessing,2008,56(1):218-232.
[125] Ahmed S, Thompson J S, Petillot Y R, et al. Unconstrained synthesis ofcovariance matrix for MIMO radar transmit beampattern. IEEE Transactions onSignal Processing,2011,59(8):3837-3849.
[126]胡亮兵，刘宏伟，杨晓超,等.集中式MIMO雷达发射方向图快速设计方法.电子与信息学报,2010,32(2):481-484.
[127] He H, Stoica P, Li J. Designing Unimodular sequence sets with goodcorrelations-including an application to MIMO radar. IEEE Transactions onSignal Processing,2009,57(11):4391-4405.
[128] Ahmed S, Thompson J S, Petillot Y R, et al. Finite alphabet constant-envelopewaveform design for MIMO radar. IEEE Transactions on Signal Processing,2011,59(11):5326-5337.
[129] Yang Y, Blum R S. MIMO radar waveform design based on mutual informationand minimum mean-square error estimation. IEEE Transactions on Aerospaceand Electronic Systems,2007,43(1):330-343.
[130] Yang Y, Blum R S. Minimax robust MIMO radar waveform design. IEEEJournal of Selected Topics in Signal Processing,2007,1(1):147-155.
[131] Yang Y, Blum R S, Zishu H, et al. MIMO radar waveform design via alternatingprojection. IEEE Transactions on Signal Processing,2010,58(3):1440-1445.
[132] Tang B, Tang J, Peng Y. MIMO radar waveform design in colored noise basedon information theory. IEEE Transactions on Signal Processing,2010,58(9):4684-4697.
[133] Tang B, Tang J, Peng Y. Waveform optimization for MIMO radar in colorednoise: further results for estimation-oriented criteria. IEEE Transactions on SignalProcessing,2012,60(3):1517-1522.
[134] Naghibi T, Behnia F. MIMO radar waveform design in the presence of clutter.IEEE Transactions on Aerospace and Electronic Systems,2011,47(2):770-781.
[135] Naghibi T, Namvar M, Behnia F. Optimal and robust waveform design forMIMO radars in the presence of clutter. Signal Processing,2010,90(4):1103-1117.
[136] Pillai S U, Oh H S, Youla D C, et al. Optimal transmit-receiver design in thepresence of signal-dependent interference and channel noise. IEEE Transactionson Information Theory,2000,46(2):577-584.
[137] Friedlander B. Waveform design for MIMO radars. IEEE Transactions onAerospace and Electronic Systems,2007,43(3):1227-1238.
[138] Chen C-Y, Vaidyanathan P P. MIMO radar waveform optimization with priorinformation of the extended target and clutter. IEEE Transactions on SignalProcessing,2009,57(9):3533-3544.
[1] Brennan L E, Reed L S. Theory of adaptive radar. IEEE Transactions onAerospace and Electronic Systems,1973,9(2):237-252.
[2] Reed I S, Mallett J D, Brennan L E. Rapid convergence rate in adaptive arrays.IEEE Transactions on Aerospace and Electronic Systems,1974,10(6):853-863.
[3] Klemm R. Optimum clutter suppression in airborne phased array radars.Proceedings of the Acoustics, Speech, and Signal Processing, IEEE InternationalConference on ICASSP '82,1982:1509-1512.
[4] Klemm R. Suboptimum clutter suppressing for airborne phased array radar.Proceedings of the IEE International Conference on Radar, London, UK,1982:473-476.
[5] Klemm R. Adaptive clutter suppression for airborne phased array radars.Communications, Radar and Signal Processing, IEE Proceedings F,1983,130(1):125-132.
[6] Klemm R. Adaptive airborne MTI: an auxiliary channel approach.Communications, Radar and Signal Processing, IEE Proceedings F,1987,134(3):269-276.
[7] Klemm R, Ender J. New aspects of airborne MTI. Proceedings of the IEEE1990InternationalRadar Conference,1990:335-340.
[8] Klemm R. Adaptive airborne MTI with two-dimensional motion compensation.Radar and Signal Processing, IEE Proceedings F,1991,138(6):551-558.
[9] Klemm R. Adaptive air-and spaceborne MTI under jamming conditions.Proceedings of the IEEE NationalRadar Conference,1993:167-172.
[10] Klemm R. Adaptive airborne MTI: comparison of sideways and forward lookingradar. Proceedings of the IEEE InternationalRadar Conference,1995:614-618.
[11] Klemm R. Real-time adaptive airborne MTI. I. Space-time processing.Proceedings of the CIE International Conference of Radar,1996:755-760.
[12] Klemm R. Real-time adaptive airborne MTI. II. Space-frequency processing.Proceedings of the CIE International Conference of Radar,1996:430-433.
[13] Klemm R. Prospectives in STAP research. Proceedings of the IEEE Sensor Arrayand Multichannel Signal Processing Workshop,2000:7-11.
[14] Klemm R. Applications of space-time adaptive processing. IEE Press,2002.
[15] Klemm R. Principles of Space-Time Adaptive Processing.3rd ed. London: TheInstitution of Engineering and Technology,2006.
[16] Bao Z, Liao G S, Wu R B, et al. Adaptive spatial-temporal processing forairborne radars.. Chinese Journal of Electronics,1993,2(1):2-7.
[17]保铮,廖桂生,吴仁彪,等.相控阵机载雷达杂波抑制的时空维自适应滤波.电子学报,1993,21(9):1-7.
[18] Bao Z, Wu S, Liao G, et al. Review of reduced rank space-time adaptiveprocessing for airborne radar. Proceedings of the CIE International Conference ofRadar,1996:766-769.
[19] Dipietro R C. Extended factored space-time processing for airborne radar systems.Proceedings of the The Twenty-Sixth Asilomar Conference on Signals, Systemsand Computers,1992:425-430.
[20] Wang H, Cai L. On adaptive spatial-temporal processing for airborne surveillanceradar systems. IEEE Transactions on Aerospace and Electronic Systems,1994,30(3):660-670.
[21] Brown R D, Wicks M C, Zhang Y, et al. A space-time adaptive processingapproach for improved performance and affordability. Proceedings of the IEEENational Radar Conference,1996:321-326.
[22] Zhang Y, Wang H. Further results of ΣΔ-STAP approach to airborne surveillanceradars. Proceedings of the IEEE NationalRadar Conference,1997:337-342.
[23]王永良,吴志文,彭应宁.适于非均匀环境的空时自适应处理方法.电子学报,1999,27(9):63-66.
[24] Ward J, Space-time adaptive processing for airborne radar.Lexington, MA,: MITLincoln Lab.,1015.1994,December.
[25] Haimovich A M, Bar-Ness Y. An eigenanalysis interference canceler. IEEETransactions on Signal Processing,1991,39(1):76-84.
[26] Haimovich A M, Berin M. Eigenanalysis-based space-time adaptive radar:performance analysis. IEEE Transactions on Aerospace and Electronic Systems,1997,33(4):1170-1179.
[27] Haimovich A. The eigencanceler: adaptive radar by eigenanalysis methods. IEEETransactions on Aerospace and Electronic Systems,1996,32(2):532-542.
[28] Goldstein J S, Reed I S. Reduced-rank adaptive filtering. IEEE Transactions onSignal Processing,1997,45(2):492-496.
[29] Goldstein J S, Reed I S. Subspace selection for partially adaptive sensor arrayprocessing. IEEE Transactions on Aerospace and Electronic Systems,1997,33(2):539-544.
[30] Goldstein J S, Reed I S. Theory of partially adaptive radar. IEEE Transactions onAerospace and Electronic Systems,1997,33(4):1309-1325.
[31] Goldstein J S, Reed I S, Scharf L L. A multistage representation of the Wienerfilter based on orthogonal projections. IEEE Transactions on Information Theory,1998,44(7):2943-2959.
[32] Brennan L E, Staudaher F M. Subclutter visibility demonstration. AdaptiveSensors Incorporated.1992.
[33] Chen C-Y, Vaidyanathan P P. MIMO radar space-time adaptive processing usingprolate spheroidal wave functions. IEEE Transactions on Signal Processing,2008,56(2):623-635.
[1] Brennan L E and Reed I S. Theory of adaptive radar. IEEE Transactions onAerospace and Electronic Systems,1973,9(2):237-252.
[2] Ward J. Space-time adaptive processing for airborne radar. MIT Lincoln Lab.,Lexington, MA, Technical Report.1015,1994.
[3] Klemm R. Principles of Space-Time Adaptive Processing.3rd Edition. London.The Institution of Engineering and Technology,2006:59-60.
[4] Richardson P G. STAP covariance matrix structure and its impact on clutter plusjamming suppression solutions. Electronics Letters,2001,37(2):118-119.
[5] Chen C-Y, Vaidyanathan P P. MIMO Radar SpaceTime Adaptive ProcessingUsing Prolate Spheroidal Wave Functions. IEEE Transactions on SignalProcessing,2008,56(2):623-635.
[6] Skolnik M I. Radar Handbook.2nd Edition. Boston, Massachusetts.McGraw-Hill,1990.
[1] Bliss D W, Forsythe K W. Multiple-input multiple-output (MIMO) radar andimaging: degrees of freedom and resolution. Proceedings of the ConferenceRecord of the Thirty-Seventh Asilomar Conference on Signals, Systems andComputers,2003:54-59.
[2] Mecca V F, Ramakrishnan D, Krolik J L. MIMO Radar Space-Time AdaptiveProcessing for Multipath Clutter Mitigation. Proceedings of the IEEE WorkshopSensor Array and Multichannel Signal Processing,2006:249-253.
[3] Wu Y, Tang J, Peng Y. Models and performance evaluation for multiple-inputmultiple-output space-time adaptive processing radar. IET Radar, Sonar&Navigation,2009,3(6):569–582.
[4]张西川,谢文冲,张永顺,等.任意波形相关性的机载MIMO雷达杂波建模与分析.电子与信息学报,2011,(3):646-651.
[5] Wang G, Lu Y. Clutter Rank of STAP in MIMO Radar With WaveformDiversity. IEEE Transactions on Signal Processing,2010,58(2):938-943.
[6] Chen C-Y, Vaidyanathan P P. MIMO Radar SpaceTime Adaptive ProcessingUsing Prolate Spheroidal Wave Functions. IEEE Transactions on SignalProcessing,2008,56(2):623-635.
[7]李彩彩,廖桂生,朱圣棋,等. MIMO雷达子阵级m-Capon方法研究.系统工程与电子技术,2010,(6):1117-1120.
[8]吕晖,冯大政,和洁,等.机载MIMO雷达两级降维杂波抑制方法.电子与信息学报,2011,(4):805-809.
[9] Ward J, Space-Time Adaptive Processing for Airborne Radar. Lexington, MA:MIT Lincoln Lab.,1015.1994, December.
[10]吕晖,冯大政,和洁,等.一种简化的机载MIMO雷达杂波特征相消器.航空学报,2011,(5):866-872.
[11] Reed I S, Mallett J D, Brennan L E. Rapid convergence rate in adaptive arrays.IEEE Transactions on Aerospace and Electonic Systems,1974,10(6):853-863.
[12] Klemm R. Principles of space-time adaptive processing.3rd ed. London: TheInstitution of Engineering and Technology,2006.
[13] Bao Z, Wu S, Liao G, et al. Review of reduced rank space-time adaptiveprocessing for airborne radar. Proceedings of the CIE International Conference ofRadar,1996:766-769.
[14] Dipietro R C. Extended factored space-time processing for airborne radar systems.Proceedings of the The Twenty-Sixth Asilomar Conference on Signals, Systemsand Computers,1992:425-430.
[15] Horn R A, Johnson C R. Matrix Analysis. Cambridge University Press,1990.
[16]吕晖.集中式MIMO雷达信号处理方法研究.西安电子科技大学,2011.
[1] Stoica P, Jian L, Yao X. On probing signal design for MIMO Radar. IEEETransactions on Signal Processing,2007,55(8):4151-4161.
[2] Tree H L V. Optimum array processing. New York: John Wiley&Sons,2002.
[3] Fuhrmann D R, San Antonio G S. Transmit beamforming for MIMO radarsystems using signal cross-correlation. IEEE Transactions on Aerospace andElectronic Systems,2008,44(1):171-186.
[4] Hassanien A, Vorobyov S A. Transmit energy focusing for DOA estimation inMIMO radar with colocated antennas. IEEE Transactions on Signal Processing,2011,59(6):2669-2682.
[5] Ahmed S, Thompson J S, Petillot Y R, et al. Unconstrained synthesis ofcovariance matrix for MIMO radar transmit beampattern. IEEE Transactions onSignal Processing,2011,59(8):3837-3849.
[6] Shadi K, Behnia F. Transmit beampattern synthesis using eigenvaluedecomposition in MIMO radar.Proceedings of the8th International Conferenceon Information, Communications and Signal Processing (ICICS),2011:1-5.
[7] Li J, Xu L, Stoica P, et al. Range compression and waveform optimization forMIMO Radar: a Cramer–Rao Bound based study. IEEE Transactions on SignalProcessing,2008,56(1):218-232.
[8] Skolnik I M. Radar handbook.2nd ed. Boston, Massachusetts: McGraw-Hill,1990.
[9] Wang G, Lu Y. Clutter Rank of STAP in MIMO radar with waveform diversity.IEEE Transactions on Signal Processing,2010,58(2):938-943.
[10]张西川,谢文冲,张永顺,等.任意波形相关性的机载MIMO雷达杂波建模与分析.电子与信息学报,2011,(3):646-651.
[11] Sturm J F. Using SeDuMi1.02, A Matlab toolbox for optimization oversymmetric cones. Optimization Methods and Software,1999,11(1-4):625-653.
[12] Grant M, Boyd S. CVX: Matlab software for disciplined convex programming.http://stanford.edu/~boyd/cvx.2008.
[13] Ward J. Space-time adaptive processing for airborne radar. MIT Lincoln Lab.,Lexington, MA, Technical Report.1015,1994.
[14] Klemm R. Principles of Space-Time Adaptive Processing.3rd Edition. London.The Institution of Engineering and Technology,2006:59-60.
[15] Richardson P G. STAP covariance matrix structure and its impact on clutter plusjamming suppression solutions. Electronics Letters,2001,37(2):118-119.
[16]张贤达.矩阵分析与应用.北京:清华大学出版社,2004.
[17] Wang G, Lu Y. Clutter Rank of STAP in MIMO radar with waveform diversity.IEEE Transactions on Signal Processing,2010,58(2):938-943.
[1] Rummler W D. Clutter suppression by complex weighting of coherent pulsetrains. IEEE Transactions on Aerospace and Electronic Systems,1966,2(6):689-699.
[2] Delong D, Hofstetter E. On the design of optimum radar waveforms for clutterrejection. IEEE Transactions on Information Theory,1967,13(3):454-463.
[3] Rummler W D. A technique for improving the clutter performance of coherentpulse train signals. IEEE Transactions on Aerospace and Electronic Systems,1967,3(6):898-906.
[4] Mitchell R L, Rihaczek A W. Clutter suppression properties of weighted pulsetrains. IEEE Transactions on Aerospace and Electronic Systems,1968,4(6):822-828.
[5] Mosca E. A nonadaptive processing of radar pulse trains for clutter rejection.IEEE Transactions on Aerospace and Electronic Systems,1969,5(6):951-958.
[6] Kay S. Optimal signal design for detection of gaussian point targets in stationarygaussian clutter/reverberation. IEEE Journal of Selected Topics in SignalProcessing,2007,1(1):31-41.
[7] Pillai S U, Oh H S, Youla D C, et al. Optimal transmit-receiver design in thepresence of signal-dependent interference and channel noise. IEEE Transactionson Information Theory,2000,46(2):577-584.
[8] Garren D A, Osborn M K, Odom A C, et al. Enhanced target detection andidentification via optimised radar transmission pulse shape. IEE Proceedings ofRadar, Sonar and Navigation,2001,148(3):130-138.
[9]纠博,刘宏伟,李丽亚,等.基于相位调制的宽带雷达波形优化方法.电子与信息学报,2008,30(9):2038-2041.
[10] Jiu B, Liu H, Feng D, et al. Minimax robust transmission waveform and receivingfilter design for extended target detection with imprecise prior knowledge. SignalProcessing,2012,92(1):210-218.
[11] Friedlander B. Waveform design for MIMO radars. IEEE Transactions onAerospace and Electronic Systems,2007,43(3):1227-1238.
[12] Chen C-Y, Vaidyanathan P P. MIMO radar waveform optimization with priorinformation of the extended target and clutter. IEEE Transactions on SignalProcessing,2009,57(9):3533-3544.
[13]庄珊娜,贺亚鹏,朱晓华.用于扩展目标检测的OFDM-MIMO雷达波形设计.南京理工大学学报,2012,(2):309-313.
[14] Naghibi T, Namvar M, Behnia F. Optimal and robust waveform design forMIMO radars in the presence of clutter. Signal Processing,2010,90(4):1103-1117.
[15] Grant M, and Boyd S. CVX: Matlab software for disciplined convexprogramming,2008.
[16] Magnus J R and Neudecker H. The commutation matrix: Some properties andapplacations. The Annals of Statistics,7(2):381-394.