在线分段波束成形 via 动态规划
Online Segmented Beamforming via Dynamic Programming
AI总结：
本文提出在线分段波束成形算法，通过动态规划框架实现动态适应，解决非平稳环境中波束成形的挑战，展现其在复杂回声环境中的优越性能。
摘要：
在动态声学环境中，有效的波束成形需要准确识别随时间变化的静止区域。传统Capon波束成形器依赖于瞬时总体协方差矩阵，这在实践中不可行。实际实现通过在时间块上平均样本协方差矩阵（SCM）来克服这一问题。然而，在非平稳设置中，朴素的批量方法失效。移动干扰源会模糊SCM，导致波束成形器在过时的位置放置零点，同时无法跟踪新出现的干扰源，从而降低其零点能力。为了解决这一根本限制，本文提出了一种在线分段波束成形器。该算法结合数据驱动的时间分段，以因果方式最小化输出功率，同时动态适应SCM估计窗口以适应局部平稳性。通过动态规划的视角，所提出的方法跟踪突发的环境变化，并在实时中重置协方差估计。我们在复杂的回声模拟声学环境和高度回声的现实世界实验中验证了该框架的性能，展示了其优于固定窗口自适应方法的优越性。
英文摘要：
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 Moving interferers smear the SCM, causing the beamformer to place nulls in outdated locations while failing to track newly active interferers, thereby degrading its nulling capabilities.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.
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