Distributed Target Detection in Compound-Gaussian Clutter under Steering Vector Uncertainty

For solving the detection challenge of distributed targets in compound-Gaussian clutter under steering vector uncertainty, we propose two efficient detectors based on the generalized likelihood ratio test (GLRT) and Wald test under the assumptions that the clutter texture is deterministic but unknown and the distributed target steering vector is confined to a specified subspace while its coordinates remain undetermined. In the parameter estimation phase, we use maximum likelihood estimation (MLE) to estimate target amplitude and texture, followed by target coordinate estimation via the projected gradient descent (PGD) method. In the detector design phase, we adopt a two-step strategy. First, we derive detectors under known clutter covariance matrix (CM). Subsequently, the CM is substituted with its approximate maximum likelihood (AML) estimate. Simulation and real-data experiments confirm that the developed detectors exhibit superior detection performance compared to existing methods, with the GLRT-based detector achieving better results than the Wald-based detector. Moreover, both detectors' detection performance improves with increased training data, reduced target distributed dimension and signal subspace dimension. In addition, the proposed detectors exhibit constant false alarm rate (CFAR) properties for texture and CM structure.