动态环境下的波束成形新方法，自动识别信号稳定段以提升抗干扰能力。
Time Segmented Beamforming via Dynamic Programming: Theory and Implementation
- 基于动态规划实现时间分段，自适应调整协方差矩阵估计窗口
- 在非平稳环境中显著提升零点抑制能力，避免干扰源拖尾效应
- 适合复杂动态声学场景，如移动机器人或智能语音设备
在时变干扰的动态声学环境中，有效波束成形需识别时间上的平稳区域。经典Capon波束成形器理论上依赖瞬时协方差矩阵，实际中采用批量Capon（或样本协方差矩阵逆）方法，通过快照块平均估计样本协方差矩阵（SCM），隐含假设块内数据平稳且可相干叠加。但在非平稳条件下，固定或过长的窗口会因移动干扰导致SCM模糊，削弱波束成形的零点性能。本文提出一种时间分段无失真响应波束成形框架，受分段最小二乘法启发，通过数据驱动方式对时间进行分段，惩罚过度分割以防止过拟合。该方法在最小化输出功率的同时，动态调整SCM估计窗口以匹配局部平稳性，为追踪时变干扰提供了理论严谨的解决方案。
原文摘要 · Abstract (English)
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.
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