Adaptive Radar Detection in Heterogeneous Clutter-dominated Environments

TL;DR

This paper introduces an adaptive radar detection method for heterogeneous clutter environments, utilizing an approximation of the generalized likelihood ratio test with an alternating estimation procedure that maintains CFAR properties.

Contribution

It proposes a novel detection architecture that handles non-Gaussian, non-homogeneous clutter by approximating the GLRT and ensuring CFAR behavior with specific initialization.

Findings

Outperforms CFAR competitors in simulations and measurements

Maintains CFAR property with specific initialization

Shows limited performance loss compared to non-CFAR detectors

Abstract

In this paper, we propose a new solution for the detection problem of a coherent target in heterogeneous environments. Specifically, we first assume that clutter returns from different range bins share the same covariance structure but different power levels. This model meets the experimental evidence related to non-Gaussian and non-homogeneous scenarios. Then, unlike existing solutions that are based upon estimate and plug methods, we propose an approximation of the generalized likelihood ratio test where the maximizers of the likelihoods are obtained through an alternating estimation procedure. Remarkably, we also prove that such estimation procedure leads to an architecture possessing the constant false alarm rate (CFAR) when a specific initialization is used. The performance analysis, carried out on simulated as well as measured data and in comparison with suitable well-known competitors, highlights that the proposed architecture can overcome the CFAR competitors and exhibits a limited loss with respect to the other non-CFAR detectors.