Adaptive Coherent Detection in Compound-Gaussian Clutter With Nakagami-Distributed Texture

This paper investigates adaptive coherent detectors in compound Gaussian clutter with Nakagami-distributed textures. The maximum a posterior ratio test (MAPRT) detector is firstly derived. Using the two-step method, the MAP-Rao/Wald detectors are derived. The three detectors have analytical expressions owing to the root formula of depressed cubic equation. The three detectors are proved to be constant false alarm rate (CFAR) with respect to the scale parameter of the texture, speckle covariance matrix, and Doppler steering vector. Their adaptive versions are shown by simulation to be approximate CFAR with estimated speckle covariance matrix by the iterative maximum likelihood (IML) estimator. The performances of the detectors are compared by using simulated and measured data. The results show that the adaptive MAP-Rao and MAPRT detectors obtains better whole performance than MAP-Wald detector and classic adaptive matched filter (AMF) and adaptive normalized matched filter (ANMF) detectors.