This paper studies adaptive detection of distributed targets in interference and noise. The signal and interference are assumed to lie in two known subspaces which are partially related; this denotes the cases where the signal and interference cannot be completely separated in spatial, temporal and frequency domains. Since impartible, the signal and interference are recast as one with the aid of singular value decomposition (SVD); then, the generalized likelihood ratio test (GLRT) and the two-step GLRT are derived in both homogeneous and partially homogeneous environments. The four new detectors are the generalizations of the existing GLRT-based ones; they have the constant false alarm rate (CFAR) properties and the capabilities of interference rejection. The effectiveness of the new detectors is demonstrated via numerical experiments, also in comparison with previous detectors of similar kind.

本文研究了干扰和噪声中分布式目标的自适应检测。假定信号和干扰位于部分相关的两个已知子空间中。这表示信号和干扰在空间，时间和频域无法完全分离的情况。由于具有可传递性，因此借助奇异值分解（SVD）可以将信号和干扰重铸为一种。然后，在均质和部分均质环境中导出广义似然比检验（GLRT）和两步GLRT。四个新的检测器是现有基于GLRT的检测器的概括；它们具有恒定的误报率（CFAR）属性和抗干扰能力。通过数值实验证明了新型探测器的有效性，