Underdetermined DOA Estimation of Off-Grid Sources Based on the Generalized Double Pareto Prior

TL;DR

This paper introduces a novel off-grid sparse Bayesian learning method using a generalized double Pareto prior to improve underdetermined DOA estimation accuracy, especially with coarse grids and limited data.

Contribution

It proposes a new GDPOGSBL approach with grid refinement and Bayesian off-grid error estimation to enhance DOA estimation in challenging scenarios.

Findings

Outperforms existing methods with coarse grids and few snapshots

Effectively mitigates grid mismatch in underdetermined scenarios

Demonstrates superior accuracy in numerical simulations

Abstract

In this letter, we investigate a new generalized double Pareto based on off-grid sparse Bayesian learning (GDPOGSBL) approach to improve the performance of direction of arrival (DOA) estimation in underdetermined scenarios. The method aims to enhance the sparsity of source signal by utilizing the generalized double Pareto (GDP) prior. Firstly, we employ a first-order linear Taylor expansion to model the real array manifold matrix, and Bayesian inference is utilized to calculate the off-grid error, which mitigates the grid dictionary mismatch problem in underdetermined scenarios. Secondly, an innovative grid refinement method is introduced, treating grid points as iterative parameters to minimize the modeling error between the source and grid points. The numerical simulation results verify the superiority of the proposed strategy, especially when dealing with a coarse grid and few snapshots.