Sparse Bayesian Learning-Based Direction Finding Method With Unknown Mutual Coupling Effect

TL;DR

This paper introduces a sparse Bayesian learning-based method called DFSMC for direction finding in array systems with unknown mutual coupling, effectively addressing off-grid errors and improving accuracy over existing methods.

Contribution

The paper proposes a novel off-grid SBL model with mutual coupling vector and an EM-based algorithm for accurate direction finding under unknown mutual coupling effects.

Findings

DFSMC outperforms existing methods in simulations.

Effective mitigation of mutual coupling effects.

Robustness to off-grid errors demonstrated.

Abstract

The imperfect array degrades the direction finding performance. In this paper, we investigate the direction finding problem in uniform linear array (ULA) system with unknown mutual coupling effect between antennas. By exploiting the target sparsity in the spatial domain, sparse Bayesian learning (SBL)-based model is proposed and converts the direction finding problem into a sparse reconstruction problem. In the sparse-based model, the \emph{off-grid} errors are introduced by discretizing the direction area into grids. Therefore, an off-grid SBL model with mutual coupling vector is proposed to overcome both the mutual coupling and the off-grid effect. With the distribution assumptions of unknown parameters including the noise variance, the off-grid vector, the received signals and the mutual coupling vector, a novel direction finding method based on SBL with unknown mutual coupling effect named DFSMC is proposed, where an expectation-maximum (EM)-based step is adopted by deriving the estimation expressions for all the unknown parameters theoretically. Simulation results show that the proposed DFSMC method can outperform state-of-the-art direction finding methods significantly in the array system with unknown mutual coupling effect.