分集MIMO雷达目标散射特性与检测算法
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摘要
多输入、多输出(MIMO)雷达是目前国际雷达领域的研究热点,它可以利用雷达发射信号多样性来提高雷达系统性能。按照雷达天线之间的距离,MIMO雷达分为两种,集中式MIMO雷达和分布式MIMO雷达,前者利用发射波形多样性提高雷达系统的自由度和分辨率,后者利用雷达目标散射的空间多样性提高雷达系统的检测性能,它又被称为空间分集雷达。另一种分集雷达是频率分集雷达,而目前同时具有空间和频率散射多样性的MIMO雷达逐渐受到重视。本论文围绕“十一五”国防预研项目“XXX/XXX雷达技术”的研究任务,研究了集中式MIMO和分布式MIMO雷达涉及的信号处理及目标检测问题,主要贡献分为以下四个部分:
研究了雷达目标回波的统计特性,尤其是相关特性,它为分集MIMO雷达信号检测器设计提供了重要依据。本文将表明,分集MIMO雷达有可能接收到既不是完全相关,也不是完全独立的目标回波信号,此时常用的独立性条件已不能用来描述目标回波信号之间的相关性的程度大小。因此,以一个频率和空间联合分集MIMO雷达为背景,本文对目标回波信号相关性的定量化进行了研究,给出了其中任意两个分集通道中目标回波信号的相关系数的表达式,该结果适用于任意形式空间分集、频率分集和空间-频率联合分集的MIMO雷达。该表达式可用于估计分集通道间目标回波信号的相关系数,为多通道融合检测算法研究和性能分析提供了基础。
集中式MIMO雷达可实现更多的自由度和更高的分辨率,但其计算量也随之大大增加。本文利用接收信号协方差阵可能具有Kronecker结构的特点,提出了相应的自适应波束形成算法,可以大大降低样本数需求和计算量。论文首先给出了干扰回波信号的协方差阵具有Kronecker结构的条件,在此基础上给出了目标参数估计算法和两种典型的信号处理结构,随后对典型干扰的协方差阵结构进行了分析,表明有源干扰信号的存在将破坏该结构。最后,通过MIMO波束形成问题,研究了无源干扰信号对基于结构化协方差阵的信号处理算法的影响,表明,基于结构化的波束形成方法可以抑制小于接收天线个数的无源干扰信号,但不能抑制多于雷达接收天线个数的干扰信号。
分集MIMO雷达通过对多通道信号的融合处理来获得检测性能的提高,不同通道间回波信号的相关特性和信噪比不同的问题是设计分集MIMO雷达信号检测算法时需要考虑的重点。本文研究了目标回波信号的相关性和信噪比不平衡对于已有检测算法的影响,并且基于尼曼-皮尔逊准则,针对目标回波相互独立但信噪比不同情形提出了一种基于信噪比加权的检测算法,以及针对目标回波统计相关且信噪比不同情形提出了一种高斯信号检测算法。最后分析了目标回波的相关性、信噪比失衡和虚警概率对于所有检测算法的影响,结果表明,目标回波的临界相关和通道信噪比失衡会降低传统检测算法的检测概率,而提出的算法能够提高雷达系统的检测性能。
实际的分集MIMO雷达检测系统有时需要考虑局部的杂波信号抑制问题,基于广义似然比准则,提出了一种具有局部干扰抑制功能的信号融合检测器,该检测器具有多个特点,包括具有恒虚警性能,可以方便的组织信号融合网络,允许不同站点的杂波信号具有不同的分布,允许不同站点的参数灵活设置,信号融合网络的中继节点可以利用过路数据提高本身的检测性能,数据通过中继节点时,信号传输带宽无需增加等,具有良好的工程应用前景。
Multiple-input multiple-output (MIMO) becomes a hot topic in the radar field recently. It can use the diversity of transmitted signals to improve radar performances. According to the distance between radar antennas, MIMO radar is categorized into two types, MIMO radar with collocated antennas and that with widely separated antennas. The former can use the signal waveform diversity to improve the degrees of freedom and angular resolution of a radar system, while the later can use the target spatial scattering diversity to improve the target detection performance, hence called spatial diversity radar. Another type of diversity radar is frequency diversity radar. The spatial-frequency jointly diversity MIMO radar, which uses both the spatial diversity technique and the frequency diversity technique, gains increasing attention. Supported by the advanced defence research program of China, named“XXX/XXX radar technique”, this work investigates signals processing and target detection algorithms for collocated MIMO radar and distributed MIMO radar. There are mainly four contributions in the work.
The statistical characteristic of radar target echoed signals, especially the target correlation characteristic, is an important concern in designing target detection algorithms for a diversity MIMO radar. It will be shown that a diversity MIMO radar may receive target echoed signals that are neither completely correlated nor independent. The widely used independence criterion becomes incapable of describing the exact degree of correlation of target echoed signals anymore. Therefore, at the background of a spatial-frequency jointly diversity MIMO radar, the correlation of target echoed signals is studied. An expression of a correlation coefficient expression to describle the correlation of target echoed signals in arbitrary two diversity channels therein is developed here. This result is applicable to any spatial diversity radar, frequency diversity radar and spatial-frequency jointly diversity MIMO radar. The expression can be used to estimate the correlation coefficient between echoed signals of a possible target, which can be used to design novel detection algorithms and evaluate existing detection algorithms.
A MIMO radar with collocated antennas can reap more degrees of freedom and achieve a higer angular resolution, at the cost of more computational cost. Based on a fact that the interference covariance matrix (ICM) may take a Kronecker production structure, an adaptive target parameter estimation algorithm and two typical signal processing schemes are proposed, which can reduce the samples size requirement and computational cost. Subsequently, the ICM structures of typical interferences are analyzed, indicating that inactive interference would spoil this structure. Via the MIMO beamforming problem, the impact of inactive interferences on MIMO beamforming algorithms based on the ICM structure is studied, indicating that such an algorithm can suppress inactive interferences fewer than receiving antennas, but can not suppress more inactive interferences.
Diversity MIMO radar can improve target detection performance by fusing signals received in multiple diversity channels. The mutual corelaiton and signal noise ratio (SNR) distribution are two major concerns in designing signal fusion based target detection algorithms for diversity MIMO radar. Their impacts on the detection performances of existing detection algorithms are studied. Moreover, based on the Newman-Pearson criterion, an SNR-weighting based detection algorithm is proposed to detect signals of different channel SNRs, and a general Gaussian signal detection algorithm to detect signals containing target echoed signals statistically correlated and of different SNRs. The impacts of the target correlation, channel SNR-unbalance and false alarm rate on concerned target detection algorithms are studied, indicating that the target correlaiton and channel SNR-unbalance can degrade conventional detection algorithms, while the proposed algorithms can improve the radar detection performance.
A practical diversity MIMO radar detection system should suppress clutter signals received in local radar sites. Based on the generalized likelihood ratio test (GLRT) criterion, a signal fusion based target detection problem with the local clutter suppression capability is proposed. The detector has many properties. It can detect targets at a constant flase alarm rate. It can be used to conveniently organize a signal fusion based radar network that can allow local covariance matrices of clutter returns receiced in widely separated radar sites to be different and local radar parameters to be flexibly set. Relay nodes therein can use passby data to improve its detection performance, without increase of the communication bandwidth when data go through relay nodes.
研究了雷达目标回波的统计特性,尤其是相关特性,它为分集MIMO雷达信号检测器设计提供了重要依据。本文将表明,分集MIMO雷达有可能接收到既不是完全相关,也不是完全独立的目标回波信号,此时常用的独立性条件已不能用来描述目标回波信号之间的相关性的程度大小。因此,以一个频率和空间联合分集MIMO雷达为背景,本文对目标回波信号相关性的定量化进行了研究,给出了其中任意两个分集通道中目标回波信号的相关系数的表达式,该结果适用于任意形式空间分集、频率分集和空间-频率联合分集的MIMO雷达。该表达式可用于估计分集通道间目标回波信号的相关系数,为多通道融合检测算法研究和性能分析提供了基础。
集中式MIMO雷达可实现更多的自由度和更高的分辨率,但其计算量也随之大大增加。本文利用接收信号协方差阵可能具有Kronecker结构的特点,提出了相应的自适应波束形成算法,可以大大降低样本数需求和计算量。论文首先给出了干扰回波信号的协方差阵具有Kronecker结构的条件,在此基础上给出了目标参数估计算法和两种典型的信号处理结构,随后对典型干扰的协方差阵结构进行了分析,表明有源干扰信号的存在将破坏该结构。最后,通过MIMO波束形成问题,研究了无源干扰信号对基于结构化协方差阵的信号处理算法的影响,表明,基于结构化的波束形成方法可以抑制小于接收天线个数的无源干扰信号,但不能抑制多于雷达接收天线个数的干扰信号。
分集MIMO雷达通过对多通道信号的融合处理来获得检测性能的提高,不同通道间回波信号的相关特性和信噪比不同的问题是设计分集MIMO雷达信号检测算法时需要考虑的重点。本文研究了目标回波信号的相关性和信噪比不平衡对于已有检测算法的影响,并且基于尼曼-皮尔逊准则,针对目标回波相互独立但信噪比不同情形提出了一种基于信噪比加权的检测算法,以及针对目标回波统计相关且信噪比不同情形提出了一种高斯信号检测算法。最后分析了目标回波的相关性、信噪比失衡和虚警概率对于所有检测算法的影响,结果表明,目标回波的临界相关和通道信噪比失衡会降低传统检测算法的检测概率,而提出的算法能够提高雷达系统的检测性能。
实际的分集MIMO雷达检测系统有时需要考虑局部的杂波信号抑制问题,基于广义似然比准则,提出了一种具有局部干扰抑制功能的信号融合检测器,该检测器具有多个特点,包括具有恒虚警性能,可以方便的组织信号融合网络,允许不同站点的杂波信号具有不同的分布,允许不同站点的参数灵活设置,信号融合网络的中继节点可以利用过路数据提高本身的检测性能,数据通过中继节点时,信号传输带宽无需增加等,具有良好的工程应用前景。
Multiple-input multiple-output (MIMO) becomes a hot topic in the radar field recently. It can use the diversity of transmitted signals to improve radar performances. According to the distance between radar antennas, MIMO radar is categorized into two types, MIMO radar with collocated antennas and that with widely separated antennas. The former can use the signal waveform diversity to improve the degrees of freedom and angular resolution of a radar system, while the later can use the target spatial scattering diversity to improve the target detection performance, hence called spatial diversity radar. Another type of diversity radar is frequency diversity radar. The spatial-frequency jointly diversity MIMO radar, which uses both the spatial diversity technique and the frequency diversity technique, gains increasing attention. Supported by the advanced defence research program of China, named“XXX/XXX radar technique”, this work investigates signals processing and target detection algorithms for collocated MIMO radar and distributed MIMO radar. There are mainly four contributions in the work.
The statistical characteristic of radar target echoed signals, especially the target correlation characteristic, is an important concern in designing target detection algorithms for a diversity MIMO radar. It will be shown that a diversity MIMO radar may receive target echoed signals that are neither completely correlated nor independent. The widely used independence criterion becomes incapable of describing the exact degree of correlation of target echoed signals anymore. Therefore, at the background of a spatial-frequency jointly diversity MIMO radar, the correlation of target echoed signals is studied. An expression of a correlation coefficient expression to describle the correlation of target echoed signals in arbitrary two diversity channels therein is developed here. This result is applicable to any spatial diversity radar, frequency diversity radar and spatial-frequency jointly diversity MIMO radar. The expression can be used to estimate the correlation coefficient between echoed signals of a possible target, which can be used to design novel detection algorithms and evaluate existing detection algorithms.
A MIMO radar with collocated antennas can reap more degrees of freedom and achieve a higer angular resolution, at the cost of more computational cost. Based on a fact that the interference covariance matrix (ICM) may take a Kronecker production structure, an adaptive target parameter estimation algorithm and two typical signal processing schemes are proposed, which can reduce the samples size requirement and computational cost. Subsequently, the ICM structures of typical interferences are analyzed, indicating that inactive interference would spoil this structure. Via the MIMO beamforming problem, the impact of inactive interferences on MIMO beamforming algorithms based on the ICM structure is studied, indicating that such an algorithm can suppress inactive interferences fewer than receiving antennas, but can not suppress more inactive interferences.
Diversity MIMO radar can improve target detection performance by fusing signals received in multiple diversity channels. The mutual corelaiton and signal noise ratio (SNR) distribution are two major concerns in designing signal fusion based target detection algorithms for diversity MIMO radar. Their impacts on the detection performances of existing detection algorithms are studied. Moreover, based on the Newman-Pearson criterion, an SNR-weighting based detection algorithm is proposed to detect signals of different channel SNRs, and a general Gaussian signal detection algorithm to detect signals containing target echoed signals statistically correlated and of different SNRs. The impacts of the target correlation, channel SNR-unbalance and false alarm rate on concerned target detection algorithms are studied, indicating that the target correlaiton and channel SNR-unbalance can degrade conventional detection algorithms, while the proposed algorithms can improve the radar detection performance.
A practical diversity MIMO radar detection system should suppress clutter signals received in local radar sites. Based on the generalized likelihood ratio test (GLRT) criterion, a signal fusion based target detection problem with the local clutter suppression capability is proposed. The detector has many properties. It can detect targets at a constant flase alarm rate. It can be used to conveniently organize a signal fusion based radar network that can allow local covariance matrices of clutter returns receiced in widely separated radar sites to be different and local radar parameters to be flexibly set. Relay nodes therein can use passby data to improve its detection performance, without increase of the communication bandwidth when data go through relay nodes.
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[1] Steven M.K..统计信号处理基础-估计与检测理论,北京:电子工业出版社,2003.
[2]张贤达,矩阵分析与应用,北京:清华大学出版社,2004.
[3] Casta?o-Martínez A., López-Blázquez F. Distribution of a Sum of Weighted Central Chi-Square Variables. Communications in Statistics - Theory and Methods. 2005, Vol. 34(3). 515 - 524.
[4] Zhou S.H., Liu H.W., Zhao Y.B., Hu L.B.“Target spatial and frequency scattering diversity property for diversity MIMO Radar”, Signal Processing, 2011, 91(2), pp. 269-276.
[5]张明友,雷达系统,北京:电子工业出版社,第二版,2006.
[6] Fishler E., Haimovich A., Blum R.S.,et al. Spatial diversity in radars-models and detection performance. Signal Processing, IEEE Transactions on. 2006, Vol. 54(3). 823-838.
[7] Robey F.C., Fuhrmann D.R., Kelly E.J.,et al. A CFAR adaptive matched filter detector. Aerospace and Electronic Systems, IEEE Transactions on. 1992, Vol. 28(1). 208-216.
[8] Kelly E.J. An Adaptive Detection Algorithm. Aerospace and Electronic Systems, IEEE Transactions on. 1986, Vol. 22(2). 115-127.
[9] Fante R.L. Multifrequency detection of a slowly fluctuating target. Aerospace and Electronic Systems, IEEE Transactions on. 1996, Vol. 32(1). 495-497.
[10] Haimovich A.M., Blum R.S., Cimini L.J. MIMO Radar with Widely Separated Antennas. Signal Processing Magazine, IEEE. 2008, Vol. 25(1). 116-129.
[11] Poularikas A.D. The handbook of formulas and tables for signal processing, Huntsville: CRC. 1998.
[1] Kelly E.J. An Adaptive Detection Algorithm. Aerospace and Electronic Systems, IEEE Transactions on. 1986, Vol. AES-22(2). 115-127.
[2] Sheikhi A., Zamani A. Adaptive Target Detection in Clutter Using MIMO Radar. Radar Symposium, 2006. IRS 2006. International2006. 1-4.
[3] Harry L Trees. Detection, Estimation and Modulation Theory, NewYork, John Wiley and Sons Inc. 2001.
[4] Alexander D. Poularikas The handbook of formulas and tables for signal processing, CRC press and IEEE press, 1998.
[5]王竹溪,郭敦仁.特殊函数概论,北京大学出版社,2000.
[6]盛骤,谢式千,潘承毅.概率论与数理统计,高等教育出版社,1989.
[7] Casta?o-Martínez A., López-Blázquez F. Distribution of a Sum of Weighted Central Chi-Square Variables. Communications in Statistics - Theory and Methods. 2005, Vol. 34(3). 515 - 524.
[8] Gradshteyn, Ryzhik. Tables of integrals, series, and products. Adademic Press, Seventh Edition, 2007.
[9] Robey F.C., Fuhrmann D.R., Kelly E.J.,et al. A CFAR adaptive matched filter detector. Aerospace and Electronic Systems, IEEE Transactions on. 1992, Vol. 28(1). 208-216.
[10] Pramod K.V.. Distributed detection and data fusion, Springer Press, 1996.