An Alternative Model for Target Position Estimation in Radar Processors

In this paper, we analyze the target position estimation errors induced by conventional radar signal processors, which assume a point-target model in matched filtering-based detection and tracking. As we demonstrated through simulations, the performance degradation under the point-target assumption can be significant for high-resolution radars, where targets extend across several detection cells. One of the main contributions of this paper is to provide an alternative model for reflections from extended targets and clutter. We model the events of backscatters from illuminated targets and clutter as a nonhomogenous Poisson process. The corresponding maximum likelihood estimator and the Cramer-Rao lower bound have been derived. Typical target detection process has been simulated and the validity of the model has been verified by using the Kullback-Leibler distance. It has been shown that the new method significantly reduces target position estimation errors compared to the peak picking method.