Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference

TL;DR

This paper introduces a Bayesian iterative algorithm for off-grid DOA estimation that improves accuracy and robustness, especially with coarse sampling grids, by accounting for off-grid effects and exploiting joint sparsity.

Contribution

It develops a novel Bayesian off-grid model and iterative algorithm that enhances DOA estimation accuracy over existing sparse methods, particularly in practical off-grid scenarios.

Findings

Improved mean squared estimation error compared to traditional methods.

High accuracy maintained even with coarse sampling grids.

Effective in both single and multi-snapshot cases.

Abstract

Direction of arrival (DOA) estimation is a classical problem in signal processing with many practical applications. Its research has recently been advanced owing to the development of methods based on sparse signal reconstruction. While these methods have shown advantages over conventional ones, there are still difficulties in practical situations where true DOAs are not on the discretized sampling grid. To deal with such an off-grid DOA estimation problem, this paper studies an off-grid model that takes into account effects of the off-grid DOAs and has a smaller modeling error. An iterative algorithm is developed based on the off-grid model from a Bayesian perspective while joint sparsity among different snapshots is exploited by assuming a Laplace prior for signals at all snapshots. The new approach applies to both single snapshot and multi-snapshot cases. Numerical simulations show that the proposed algorithm has improved accuracy in terms of mean squared estimation error. The algorithm can maintain high estimation accuracy even under a very coarse sampling grid.