MIMO Radar Detection in Non-Gaussian and Heterogeneous Clutter
MIMO Radar Detection in Non-Gaussian and Heterogeneous Clutter
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DOI：
10.1109/jstsp.2009.2038980
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发表时间：
2010-01
影响因子：
7.5
通讯作者：
C. Y. Chong;F. Pascal;J. Ovarlez;M. Lesturgie
中科院分区：
文献类型：
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作者：
C. Y. Chong;F. Pascal;J. Ovarlez;M. Lesturgie
本文将广义似然比检验线性二次型(GLRT-LQ)推广到多输入多输出(MIMO)情况，其中所有发射-接收子阵列被联合视为一个系统，使得只使用一个检测门限。GLRT-LQ检测器是基于球不变随机向量(SIRV)模型推导的，它相对于杂波功率起伏(也称为纹理)具有恒虚警率(CFAR)。然后，新的MIMO检测器也被证明是纹理恒虚警。首先对这种新检测器的理论性能进行了解析推导，然后用蒙特卡罗模拟对其进行了验证。然后将其与著名的最佳高斯检测器(OGD)在高斯杂波和非高斯杂波下的检测性能进行了比较。接下来，研究了检测器的自适应版本。协方差矩阵的估计使用固定点(FP)算法，使得检测器能够保持纹理和矩阵的恒虚警。研究了协方差矩阵的估计对检测性能的影响。
In this paper, the generalized likelihood ratio test-linear quadratic (GLRT-LQ) has been extended to the multiple-input multiple-output (MIMO) case where all transmit-receive subarrays are considered jointly as a system such that only one detection threshold is used. The GLRT-LQ detector has been derived based on the spherically invariant random vector (SIRV) model and is constant false alarm rate (CFAR) with respect to the clutter power fluctuations (also known as the texture). The new MIMO detector is then shown to be texture-CFAR as well. The theoretical performance of this new detector is first analytically derived and then validated using Monte Carlo simulations. Its detection performance is then compared to that of the well-known Optimum Gaussian Detector (OGD) under Gaussian and non-Gaussian clutter. Next, the adaptive version of the detector is investigated. The covariance matrix is estimated using the Fixed Point (FP) algorithm which enables the detector to remain texture- and matrix-CFAR. The effects of the estimation of the covariance matrix on the detection performance are also investigated.