Clutter Edge Detection and Estimation of Field Parameters in Radar detection

In this paper we propose three detectors which decide between non-homogenous and homogenous hypotheses of a Gaussian field. We derive the optimal Neyman-Pearson bound and also ALR, GLR and HLR tests for this problem. The simulation results show that the ALR detector is so close to the Neyman-Pearson bound and the performance of ALR and HLR detectors is better than that of GLR. These results show that the edge of non-homogeneousness can be approximately modeled by a discrete uniform random variable, (i.e., this assumption causes more signal processing gain than that of deterministic model). In this paper, we also derive an ML estimation of nonhomogeneousness edge. Finally, we propose an extended CFAR method for radar target detection