Pseudo-Optimum CFAR Detectors in non-Gaussian Clutter

In this paper, the problem of signal detection based on suboptimum-constant false alarm rate (CFAR) detector, in the presence of a mixture of K-distributed clutter, is studied. In this case, a new suboptimum detector named generalized likelihood ratio test and maximum a posteriori (GLRT-MAP) is proposed and compared with generalized likelihood ratio test and linear quadratic (GLRT-LQ) suboptimum detector. The CFAR properties of the GLRT-MAP detector are investigated and compared with that of the GLRT-LQ detector. The simulation results show that the GLRT-MAP is a completely CFAR detector regardless of the clutter distribution and correlation in covariance matrix, whereas the GLRT-LQ is only CFAR detector regardless to clutter distribution. The performance analyses of the GLRT-MAP and GLRT-LQ are investigated by means of Monte Carlo simulation, and results are provided in results section.