In dynamic acoustic environments with time-varying interferers, effective beamforming requires identifying stationary regions over time. The Capon beamformer, a whitened matched filter constrained to maintain unity gain in the desired direction, theoretically relies on the instantaneous ensemble covariance matrix. Practical implementations rely on the batch Capon (or Sample Matrix Inversion), which estimates the sample covariance matrix (SCM) by averaging over a block of snapshots. This practical approach implicitly assumes that the data within the batch window is stationary and can be coherently combined. In non-stationary settings, a batch approach that averages over fixed or excessively long windows fails, as moving interferers smear the SCM and degrade the beamformer's nulling capabilities. To address this, this paper introduces a temporally segmented distortionless response beamformer. Inspired by the segmented least squares method, which fits piecewise polynomials to data while penalizing excessive segmentation to prevent overfitting, the framework extends practical Capon beamforming by incorporating data-driven temporal segmentation. This formulation minimizes output power while dynamically adapting the SCM estimation windows to local stationarity, offering a principled approach to tracking time-varying interferers.

翻译：在具有时变干扰的动态声学环境中，有效的波束形成需要识别随时间变化的平稳区域。Capon波束形成器作为一种白化匹配滤波器，其设计约束是维持期望方向的单位增益，理论上依赖于瞬时总体协方差矩阵。实际应用中采用批处理Capon方法（或样本矩阵求逆），通过对一组快拍数据求平均来估计样本协方差矩阵（SCM）。这种实用方法隐含假设批处理窗口内的数据是平稳的，可以相干合并。在非平稳场景中，对固定或过长窗口求平均的批处理方法会失效，因为移动干扰会模糊SCM并降低波束形成器的零陷能力。为此，本文提出一种时间分段无失真响应波束形成器。该框架受分段最小二乘法的启发——该方法通过分段多项式拟合数据，并对过度分段施加惩罚以防止过拟合——将数据驱动的时间分段思想扩展到实用型Capon波束形成。该公式在最小化输出功率的同时，使SCM估计窗口动态适应局部平稳性，为跟踪时变干扰提供了一种原则性方法。