Target Detection With Imperfect Waveform Separation in Distributed MIMO Radar
Target Detection With Imperfect Waveform Separation in Distributed MIMO Radar
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DOI：
10.1109/tsp.2020.2964227
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发表时间：
2020-01-01
影响因子：
5.4
通讯作者：
Li, Hongbin
中科院分区：
文献类型：
--
作者：
Wang, Pu;Li, Hongbin
研究了分布式多输入多输出(MIMO)雷达在本地接收端波形分离不理想情况下的目标检测问题。该问题被描述为一个二元复合假设检验问题，其中由于波形分离不完善而导致的目标残差被显式地建模为备选假设中的子空间分量，而包括杂波和热噪声在内的干扰在这两个假设下都存在。在目标幅度在一次扫描过程中起伏和不起伏的假设下，特别考虑了分布式混合阶高斯(DHOG)信号模型，提出了广义似然比检验(GLRT)，它依赖于目标幅度的极大似然(ML)估计和备选假设下的残差协方差矩阵。推导了估计目标幅度和剩余子空间协方差矩阵的Cramer-Rao界(CRB)。在本地和分布式场景中的仿真结果证实了所提出的GLRT算法的有效性，并通过利用目标残差分量的存在改善了接收机工作特性(ROC)的性能。
This paper considers target detection in distributed multiple-input multiple-output (MIMO) radar with imperfect waveform separation at local receivers. The problem is formulated as a binary composite hypothesis testing problem, where target residuals due to imperfect waveform separation are explicitly modeled as a subspace component in the alternative hypothesis, while disturbances including the clutter and thermal noise are present under both hypotheses. Under assumptions of fluctuating and non-fluctuating target amplitude over a scan, e.g., Swerling models, we particularly consider a distributed hybrid-order Gaussian (DHOG) signal model and develop the generalized likelihood ratio test (GLRT) which relies on the maximum likelihood (ML) estimation of the target amplitude and the residual covariance matrix under the alternative hypothesis. The Cramer-Rao bounds (CRBs) on estimating the target amplitude and residual subspace covariance matrix are derived. Simulation results in both local and distributed scenarios confirm the effectiveness of the proposed GLRT and show improved performance in terms of receiver operating characteristic (ROC) by exploiting the existence of target residual component.