Robust and Sparse M-Estimation of DOA

TL;DR

This paper introduces a robust and sparse DOA estimation method for array data modeled by CES distributions, utilizing various loss functions including Gaussian, MVT, Huber, and Tyler, with a focus on robustness and sparsity.

Contribution

It develops a flexible DOA estimator based on M-estimation that can adapt to different noise models and includes a robust sparse Bayesian learning approach.

Findings

Robust SBL estimators perform well across different noise models.

The method reduces to classical SBL under Gaussian noise.

Root mean square DOA error is analyzed for various distributions.

Abstract

A robust and sparse Direction of Arrival (DOA) estimator is derived for array data that follows a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order moments. The derivation allows to choose the loss function and four loss functions are discussed in detail: the Gauss loss which is the Maximum-Likelihood (ML) loss for the circularly symmetric complex Gaussian distribution, the ML-loss for the complex multivariate $t$-distribution (MVT) with $\nu$ degrees of freedom, as well as Huber and Tyler loss functions. For Gauss loss, the method reduces to Sparse Bayesian Learning (SBL). The root mean square DOA error of the derived estimators is discussed for Gaussian, MVT, and $\epsilon$-contaminated data. The robust SBL estimators perform well for all cases and nearly identical with classical SBL for Gaussian noise.