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 Cramér-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.

中文翻译：

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分布式MIMO雷达中不完全波形分离的目标检测

本文考虑了分布式多输入多输出 (MIMO) 雷达中的目标检测，在本地接收器处具有不完美的波形分离。该问题被表述为二元复合假设检验问题，其中由于不完美的波形分离导致的目标残差被明确建模为替代假设中的子空间分量，而在两种假设下都存在包括杂波和热噪声在内的干扰。在扫描过程中波动和非波动目标幅度的假设下，例如 Swerling 模型，我们特别考虑了分布式混合阶高斯 (DHOG) 信号模型，并开发了依赖于最大似然的广义似然比检验 (GLRT)（ ML) 在备择假设下对目标幅度和残差协方差矩阵的估计。推导出用于估计目标幅度和残差子空间协方差矩阵的 Cramér-Rao 界限 (CRB)。本地和分布式场景中的仿真结果证实了所提出的 GLRT 的有效性，并通过利用目标残差分量的存在显示了接收器操作特性 (ROC) 方面的改进性能。