Since frequency diverse array multiple-input multiple-output (FDA-MIMO) radar possesses additional target range information for potential performance improvement, this article studies adaptive distributed targets detection for FDA-MIMO radar, where the targets are embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model considers also the distributed targets occupying several secondary range cells, which is different from the classic detection models in multiple-input multiple-output (MIMO) and/or phase array (PA) radars that discuss only point-like targets. By exploiting the FDA-MIMO radar framework for distributed target detection, we propose the detector through a two-step generalized likelihood ratio test criteria without the need of training data and/or a priori covariance matrix. Moreover, closed-form expressions for the probability of false alarm and detection probability are derived, respectively. The proposed detector adheres to the property of a constant false alarm rate because its probability of false alarm is not restricted by the covariance matrix. The proposed method together with all theoretical analysis are verified by numerical results.

中文翻译：

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无训练数据的高斯杂波中 FDA-MIMO 雷达的自适应分布式目标检测


由于频率不同阵列多输入多输出 (FDA-MIMO) 雷达拥有额外的目标距离信息，可以提高潜在的性能，因此本文研究了 FDA-MIMO 雷达的自适应分布式目标检测，其中目标嵌入到协方差未知的高斯杂波中矩阵。所提出的 FDA-MIMO 雷达检测模型还考虑了占据多个辅助距离单元的分布式目标，这与多输入多输出 (MIMO) 和/或相控阵 (PA) 雷达中仅讨论点的经典检测模型不同。类似的目标。通过利用 FDA-MIMO 雷达框架进行分布式目标检测，我们通过两步广义似然比测试标准提出检测器，而不需要训练数据和/或先验协方差矩阵。此外，分别导出了虚警概率和检测概率的封闭式表达式。所提出的检测器遵循恒定误报率的特性，因为其误报概率不受协方差矩阵的限制。数值结果验证了所提出的方法以及所有理论分析。