Online Segmented Beamforming via Dynamic Programming

TL;DR

This paper introduces an online segmented beamforming algorithm that adaptively updates covariance estimates in non-stationary acoustic environments using dynamic programming, improving nulling of interferers.

Contribution

It presents a novel data-driven segmentation approach with dynamic programming for real-time covariance estimation in moving source scenarios.

Findings

Outperforms fixed-window adaptive methods in simulated environments.

Effectively tracks abrupt environmental changes in real-time.

Demonstrates superior nulling capabilities in real-world reverberant settings.

Abstract

In dynamic acoustic environments characterized by time-varying interferers and moving sources, effective beamforming requires accurately identifying stationary regions over time. Traditional Capon beamformers rely on the instantaneous ensemble covariance matrix, which is inaccessible in practice. Practical implementations overcome this by estimating the sample covariance matrix (SCM) through averaging over a block of temporal samples. However, in non-stationary settings, a naive batch approach fails. Moving interferers smear the SCM, causing the beamformer to place nulls in outdated locations while failing to track newly active interferers, thereby degrading its nulling capabilities. To address this fundamental limitation, an Online Segmented Beamformer is proposed. This algorithm incorporates data-driven temporal segmentation to causally minimize output power while dynamically adapting the SCM estimation windows to local stationarity. By framing the problem through the lens of dynamic programming, the proposed method tracks abrupt environmental changes and resets covariance estimates in real-time. We validate the performance of this framework in a complex, reverberant simulated acoustic environment and in highly reverberant real world experiments, demonstrating its superiority over fixed-window adaptive methods.