Target range estimation based on a non‐homogenous Poisson process model

Abstract

In this study, the author analyses target range estimation errors in matched filtering-based detection performed in high range resolution (HRR) radars. Conventional radar signal processors use point target detectors, where extended target responses are put through a point detection process by windowing and thresholding. The author demonstrates through simulations that the performance of degradation under the point target assumption can be significant for HRR radars, where targets extend across several detection cells. The author modelled the reflections for stationary and moving extended target scenarios by using three target signal models (TSMs). A non-homogenous Poisson process (NHPP) is provided to model the signal at the output of the target detector, which includes reflections from targets and clutter. The corresponding maximum likelihood (ML) estimator is derived as a range estimation technique. The author simulated a target detection process and made comparisons between the classical- and NHPP-based peak estimator performances for each of the TSMs. Furthermore, the ML estimation (MLE) algorithm is extended for multiple targets. The author demonstrates that the ML estimator significantly reduces target range estimation errors compared with the classical point target estimators.