Root Sparse Bayesian Learning for Off-Grid DOA Estimation

TL;DR

This paper introduces a computationally efficient root-based sparse Bayesian learning method for off-grid DOA estimation, significantly reducing complexity while maintaining high accuracy through iterative grid refinement.

Contribution

A novel root SBL approach using an EM algorithm for off-grid DOA estimation that enhances speed and accuracy compared to existing methods.

Findings

Reduced computational complexity

Near-elimination of modeling error

Effective iterative grid refinement

Abstract

The performance of the existing sparse Bayesian learning (SBL) methods for off-gird DOA estimation is dependent on the trade off between the accuracy and the computational workload. To speed up the off-grid SBL method while remain a reasonable accuracy, this letter describes a computationally efficient root SBL method for off-grid DOA estimation, where a coarse refinable grid, whose sampled locations are viewed as the adjustable parameters, is adopted. We utilize an expectation-maximization (EM) algorithm to iteratively refine this coarse grid, and illustrate that each updated grid point can be simply achieved by the root of a certain polynomial. Simulation results demonstrate that the computational complexity is significantly reduced and the modeling error can be almost eliminated.