DOA Estimation in Nonuniform Sensor Noise

TL;DR

This paper introduces a new three-phase algorithm for accurate DOA estimation in nonuniform sensor noise environments, combining noise covariance estimation, a novel DOA estimator, and a source selection strategy, outperforming existing methods.

Contribution

The paper presents a novel iterative algorithm that jointly estimates nonuniform sensor noise and DOA, with improved accuracy and computational efficiency over prior approaches.

Findings

The proposed algorithm achieves higher accuracy in DOA estimation under nonuniform noise.

It requires only a few iterations for convergence, reducing computational cost.

Numerical simulations show superior performance compared to state-of-the-art methods.

Abstract

The problem of direction-of-arrival (DOA) estimation in the presence of nonuniform sensor noise is considered and a novel algorithm is developed. The algorithm consists of three phases. First, the diagonal nonuniform sensor noise covariance matrix is estimated using an iterative procedure that requires only few iterations to obtain an accurate estimate. The asymptotic variance of one iteration is derived for the proposed noise covariance estimator. Second, a forward-only rooting-based DOA estimator as well as its forward-backward averaging extension are developed for DOA estimation. The DOA estimators take advantage of using second-order statistics of signal subspace perturbation in constructing a weight matrix of a properly designed generalized least squares minimization problem. Despite the fact that these DOA estimators are iterative, only a few iterations are sufficient to reach accurate results. The asymptotic performance of these DOA estimators is also investigated. Third, a newly designed DOA selection strategy with reasonable computational cost is developed to select L actual sources out of 2L candidates generated at the second phase. Numerical simulations are conducted in order to establish the considerable superiority of the proposed algorithm compared to the the existing state-of-the-art methods in challenging scenarios in both cases of uniform and nonuniform sensor noise.