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.

翻译：自适应波束形成是阵列信号处理的基石，但其性能在面对复杂且快速变化的干扰时常常失效。当干扰源不可预测地出现或移动时，传统估计器面临根本性的记忆权衡：短时间窗可实现快速跟踪但估计方差大，长时间窗能提供稳定抑制却难以适应变化。这一问题通过引入通用切换波束形成器（USB）得以解决，它将竞争性序贯预测融入波束形成架构中。USB利用线性转移图隐式维护指数级大的候选协方差历史族，并根据其累积输出功率动态重新加权。该机制使波束形成器能自动调整有效记忆长度，无需显式变化检测或启发式参数调优。证明了相对于事后选择最佳分段平稳协方差模型的全知先知的理论遗憾上界。在SwellEx-96数据集上的大量仿真和实验表明，USB兼具短时间窗估计器的敏捷性与长时间积分估计器的精度，为跟踪高度非平稳场景提供了原则性解决方案。