Message Passing Based Block Sparse Signal Recovery for DOA Estimation Using Large Arrays

TL;DR

This paper introduces a message passing Bayesian algorithm for DOA estimation using large arrays, leveraging a novel sparse signal model and structured block sparsity to improve performance and reduce complexity.

Contribution

It presents a new signal model with a sparse transfer matrix and a message passing algorithm tailored for structured block sparse recovery in DOA estimation.

Findings

Superior performance demonstrated in simulations

Low complexity due to message passing approach

Effective handling of large array data

Abstract

This work deals with directional of arrival (DOA) estimation with a large antenna array. We first develop a novel signal model with a sparse system transfer matrix using an inverse discrete Fourier transform (DFT) operation, which leads to the formulation of a structured block sparse signal recovery problem with a sparse sensing matrix. This enables the development of a low complexity message passing based Bayesian algorithm with a factor graph representation. Simulation results demonstrate the superior performance of the proposed method.