Iterative Sparse Asymptotic Minimum Variance Based Approaches for Array Processing

TL;DR

This paper introduces iterative, parameter-free SAMV array processing methods that improve DOA estimation accuracy and robustness, especially with limited snapshots and arbitrary array geometries, by jointly estimating signal powers and noise variance.

Contribution

The paper develops novel iterative SAMV approaches that are robust against insufficient snapshots, coherent sources, and arbitrary array geometries, and introduces SAMV-SML for gridless estimation.

Findings

SAMV approaches outperform existing methods in various scenarios.

Proposed methods are robust with limited snapshots and arbitrary array geometries.

Approximate solutions are effective at different SNR levels.

Abstract

This paper presents a series of user parameter-free iterative Sparse Asymptotic Minimum Variance (SAMV) approaches for array processing applications based on the asymptotically minimum variance (AMV) criterion. With the assumption of abundant snapshots in the direction-of-arrival (DOA) estimation problem, the signal powers and noise variance are jointly estimated by the proposed iterative AMV approach, which is later proved to coincide with the Maximum Likelihood (ML) estimator. We then propose a series of power-based iterative SAMV approaches, which are robust against insufficient snapshots, coherent sources and arbitrary array geometries. Moreover, to overcome the direction grid limitation on the estimation accuracy, the SAMV-Stochastic ML (SAMV-SML) approaches are derived by explicitly minimizing a closed form stochastic ML cost function with respect to one scalar parameter, eliminating the need of any additional grid refinement techniques. To assist the performance evaluation, approximate solutions to the SAMV approaches are also provided at high signal-to-noise ratio (SNR) and low SNR, respectively. Finally, numerical examples are generated to compare the performance of the proposed approaches with existing approaches.