This paper studies adaptive radar detection of range distributed targets in the presence of Gaussian interference and possible diffuse multipath returns modeled as independent zero-mean complex circular Gaussian random vectors with unknown covariance matrices. For this problem, an adaptive constrained generalized likelihood ratio (ACGLR) test is devised, where in each range cell of the primary data the covariance matrix (due to both multipath and disturbance echoes) is forced to belong to a neighborhood of the secondary data sample covariance. The size of the uncertainty set is determined adaptively employing jointly a union-intersection test and an expectation likelihood (EL)-based estimate. Besides, an adaptive detector based on the complex parameter Rao test criterion is derived. Remarkably, both the two new architectures possess the desired constant false alarm rate (CFAR) property with respect to the disturbance covariance. Finally, their detection performance is assessed and validated via numerical examples.

中文翻译：

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用于距离分布式目标自适应检测的扩散多路径利用


本文研究了在存在高斯干扰和可能的扩散多径返回的情况下对距离分布目标的自适应雷达检测，该多径返回被建模为具有未知协方差矩阵的独立零均值复圆高斯随机向量。对于这个问题，设计了一种自适应约束广义似然比（ACGLR）测试，其中在主要数据的每个范围单元中，协方差矩阵（由于多径和干扰回波）被迫属于辅助数据样本的邻域协方差。联合相交测试和基于期望似然 (EL) 的估计自适应地确定不确定性集的大小。此外，还推导了一种基于复参数Rao检验准则的自适应检测器。值得注意的是，这两种新架构都具有所需的关于干扰协方差的恒定误报率 (CFAR) 属性。最后，通过数值例子评估和验证它们的检测性能。