This paper investigates the problem of adaptive detection of distributed targets in power heterogeneous clutter. In the considered scenario, all the data share the identical structure of clutter covariance matrix, but with varying and unknown power mismatches. To address this problem, we iteratively estimate all the unknowns, including the coordinate matrix of the target, the clutter covariance matrix, and the corresponding power mismatches, and propose three detectors based on the generalized likelihood ratio test (GLRT), Rao and the Wald tests. The results from simulated and real data both illustrate that the detectors based on GLRT and Rao test have higher probabilities of detection (PDs) than the existing competitors. Among them, the Rao test-based detector exhibits the best overall detection performance. We also analyze the impact of the target extended dimensions, the signal subspace dimensions, and the number of training samples on the detection performance. Furthermore, simulation experiments also demonstrate that the proposed detectors have a constant false alarm rate (CFAR) property for the structure of clutter covariance matrix.

翻译：本文研究了功率异质杂波中分布式目标的自适应检测问题。在所考虑的场景中，所有数据共享相同的杂波协方差矩阵结构，但存在未知且变化的功率失配。为解决此问题，我们迭代估计所有未知参数，包括目标坐标矩阵、杂波协方差矩阵以及相应的功率失配因子，并基于广义似然比检验（GLRT）、Rao检验和Wald检验提出了三种检测器。仿真和实测数据结果均表明，基于GLRT和Rao检验的检测器比现有方法具有更高的检测概率。其中，基于Rao检验的检测器展现出最佳的整体检测性能。我们还分析了目标扩展维度、信号子空间维度和训练样本数量对检测性能的影响。此外，仿真实验也证明，所提出的检测器对杂波协方差矩阵结构具有恒虚警率（CFAR）特性。