A Switching Beamformer for Highly Non-Stationary Environments

2026-06-06

AI Summary

This work addresses the severe performance degradation of conventional adaptive beamformers under highly nonstationary interference, which stems from the inherent trade-off between memory length and estimation variance. To overcome this limitation, the authors propose the Universal Switching Beamformer (USB), which dynamically integrates an exponential family of covariance history models through a competitive sequence prediction mechanism. USB adaptively adjusts its effective memory length based on accumulated output power, automatically balancing tracking speed and estimation accuracy without requiring explicit change detection or heuristic parameter tuning. The method is accompanied by a theoretical regret bound relative to the best piecewise-stationary hindsight benchmark. Experimental results on the SwellEx-96 dataset demonstrate that USB achieves both the agility of short-window estimators and the interference suppression capability of long-window estimators, yielding significant performance gains in nonstationary environments.

Abstract

Adaptive beamforming is a cornerstone of array signal processing, yet its performance often collapses in the face of complex, rapidly changing interference. When interferers appear or move unpredictably, conventional estimators encounter a fundamental memory trade-off: short windows enable rapid tracking but suffer from high estimation variance, while long windows provide stable rejection but fail to adapt to shifts. This challenge is resolved by introducing the Universal Switching Beamformer (USB), which integrates competitive sequential prediction into the beamforming architecture. By employing a linear transition diagram, the USB implicitly maintains an exponentially large family of candidate covariance histories and dynamically re-weights them based on their cumulative output power. This mechanism allows the beamformer to automatically vary its effective memory length without explicit change detection or heuristic parameter tuning. A theoretical upper bound is proven on the regret relative to an omniscient oracle that selects the best piecewise-stationary covariance model in hindsight. Extensive simulations and experiments on the SwellEx-96 dataset demonstrate that the USB achieves the agility of short-window estimators and the precision of long-term integration, providing a principled solution for tracking highly non-stationary scenes.

Problem

• adaptive beamforming
• non-stationary environments
• interference tracking
• covariance estimation
• memory trade-off

Innovation

• Switching Beamformer
• Non-stationary Environments
• Sequential Prediction
• Covariance Estimation
• Adaptive Beamforming