Direction-of-Arrival and Noise Covariance Matrix joint estimation for beamforming

TL;DR

This paper introduces a joint estimation method for DoA and Noise Covariance Matrix that simplifies the process, improves robustness across frequencies, and outperforms classical techniques like MUSIC in noisy, reverberant environments.

Contribution

A novel joint estimation approach that simplifies DoA and NCM estimation, operating across all frequency bins for enhanced robustness and performance.

Findings

Outperforms classical methods like MUSIC in angular accuracy

Achieves better noise rejection and interference cancellation

Demonstrates superior signal enhancement in simulations

Abstract

We propose a joint estimation method for the Direction-of-Arrival (DoA) and the Noise Covariance Matrix (NCM) tailored for beamforming applications. Building upon an existing NCM framework, our approach simplifies the estimation procedure by deriving an quasi-linear solution, instead of the traditional exhaustive search. Additionally, we introduce a novel DoA estimation technique that operates across all frequency bins, improving robustness in reverberant environments. Simulation results demonstrate that our method outperforms classical techniques, such as MUSIC, in mid- to high-angle scenarios, achieving lower angular errors and superior signal enhancement through beamforming. The proposed framework was also fared against other techniques for signal enhancement, having better noise rejection and interference canceling capabilities. These improvements are validated using both theoretical and empirical performance metrics.