Low-Rank and Row-Sparse Decomposition for Joint DOA Estimation and Distorted Sensor Detection

TL;DR

This paper introduces a low-rank and row-sparse decomposition method with an IRLS algorithm for joint DOA estimation and distorted sensor detection, outperforming existing techniques in speed and accuracy.

Contribution

It proposes a novel IRLS-based approach for simultaneous DOA estimation and sensor fault detection under sensor distortion, with proven convergence and superior performance.

Findings

IRLS algorithm effectively detects sensor distortions.

Method outperforms state-of-the-art in convergence and accuracy.

Approach is robust in noisy and noiseless scenarios.

Abstract

Distorted sensors could occur randomly and may lead to the breakdown of a sensor array system. We consider an array model within which a small number of sensors are distorted by unknown sensor gain and phase errors. With such an array model, the problem of joint direction-of-arrival (DOA) estimation and distorted sensor detection is investigated and the problem is formulated under the framework of low-rank and row-sparse decomposition. We derive an iteratively reweighted least squares (IRLS) algorithm to solve the resulting problem in both noiseless and noisy cases. The convergence property of the IRLS algorithm is analyzed by means of the monotonicity and boundedness of the objective function. Extensive simulations are conducted regarding parameter selection, convergence speed, computational complexity, and performances of DOA estimation as well as distorted sensor detection. Even though the IRLS algorithm is slightly worse than the alternating direction method of multipliers in detecting the distorted sensors, the results show that our approach outperforms several state-of-the-art techniques in terms of convergence speed, computational cost, and DOA estimation performance.