NUV-DoA: NUV Prior-based Bayesian Sparse Reconstruction with Spatial Filtering for Super-Resolution DoA Estimation

TL;DR

The paper introduces NUV-DoA, a Bayesian sparse reconstruction method with spatial filtering that achieves super-resolution DoA estimation, especially effective in low SNR conditions, by modeling directions with NUV priors and simplifying the optimization process.

Contribution

It proposes a novel NUV prior-based Bayesian approach combined with spatial filtering for super-resolution DoA estimation, reducing complex optimization to iterative reweighted least-squares.

Findings

Outperforms existing DoA estimators in low SNR scenarios.

Accurately detects multiple source directions with interference cancellation.

Simplifies the optimization process for high-resolution DoA estimation.

Abstract

Achieving high-resolution Direction of Arrival (DoA) recovery typically requires high Signal to Noise Ratio (SNR) and a sufficiently large number of snapshots. This paper presents NUV-DoA algorithm, that augments Bayesian sparse reconstruction with spatial filtering for super-resolution DoA estimation. By modeling each direction on the azimuth's grid with the sparsity-promoting normal with unknown variance (NUV) prior, the non-convex optimization problem is reduced to iteratively reweighted least-squares under Gaussian distribution, where the mean of the snapshots is a sufficient statistic. This approach not only simplifies our solution but also accurately detects the DoAs. We utilize a hierarchical approach for interference cancellation in multi-source scenarios. Empirical evaluations show the superiority of NUV-DoA, especially in low SNRs, compared to alternative DoA estimators.