Online Segmented Beamforming via Dynamic Programming
For practitioners of adaptive beamforming in non-stationary acoustic environments, this work provides a practical online algorithm that improves interference suppression by dynamically adjusting to local stationarity.
The paper tackles the problem of beamforming in dynamic acoustic environments with moving sources and interferers, where traditional fixed-window methods fail. The proposed online segmented beamformer uses dynamic programming to adaptively segment data and update covariance estimates, achieving superior nulling performance over fixed-window methods in simulated and real-world reverberant environments.
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 Moving interferers smear the SCM, causing the beamformer to place nulls in outdated locations while failing to track newly active interferers, thereby degrading its nulling capabilities.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.