Maximum Likelihood Detector in Gamma-Distributed Sea Clutter

Abstract

Constant false alarm rate (CFAR) is the desired property for automatic target detection in unknown and nonstationary background. In this letter, an analysis of the experimental data shows that gamma (GM) distribution is a promising model for sea clutter. Furthermore, a modified cell-averaging (CA) detector for GM-distributed clutter is proposed by using the maximum likelihood estimation method. Theoretical analysis demonstrates that the proposed detector maintains the CFAR property with respect to the scale parameter of the GM-distributed background. The proposed detector is verified to be optimal in homogenous GM-distributed clutter with a known shape parameter when compared with CA, greatest of selection, ordered statistic (OS), and weighted amplitude iteration (WAI) detectors. At clutter edges, the proposed method attains a similar false alarm rate control compared with the CA, OS, and WAI detectors. In multiple-target scenario, the proposed method works effectively and robustly, whereas the competitors suffer performance degradation in varying degree. Simulation and experimental results demonstrate the superiority and generality of the proposed method.