Robust Burg Estimation of Radar Scatter Matrix for Autoregressive structured SIRV based on Fr\'echet medians

TL;DR

This paper introduces a robust Burg estimation method for the scatter matrix of autoregressive structured SIRV models, improving performance in non-Gaussian and contaminated data scenarios, especially for radar applications.

Contribution

It adapts Burg algorithms for scale mixtures of autoregressive vectors using Fréchet medians, enhancing robustness against outliers and non-Gaussian noise.

Findings

Improved estimation accuracy in non-Gaussian settings

Enhanced robustness with Fréchet median-based modifications

Better radar detection performance compared to traditional methods

Abstract

We address the estimation of the scatter matrix of a scale mixture of Gaussian stationary autoregressive vectors. This is equivalent to consider the estimation of a structured scatter matrix of a Spherically Invariant Random Vector (SIRV) whose structure comes from an autoregressive modelization. The Toeplitz structure representative of stationary models is a particular case for the class of structures we consider. For Gaussian autoregressive processes, Burg method is often used in case of stationarity for its efficiency when few samples are available. Unfortunately, if we directly apply these methods to estimate the common scatter matrix of N vectors coming from a non-Gaussian distribution, their efficiency will strongly decrease. We propose then to adapt these methods to scale mixtures of autoregressive vectors by changing the energy functional minimized in the Burg algorithm. Moreover, we study several approaches of robust modification of the introduced Burg algorithms, based on Fr\'echet medians defined for the Euclidean or the Poincar\'e metric, in presence of outliers or contaminating distributions. The considered structured modelization is motivated by radar applications, the performances of our methods will then be compared to the very popular Fixed Point estimator and OS-CFAR detector through radar simulated scenarios.