arXiv:2510.00934eess.SPeess.AS2025-10

提出主动通信策略,让降噪节点自适应切换滤波模式,稳定提升分布式降噪效果。

A Robust Proactive Communication Strategy for Distributed Active Noise Control Systems

  • 节点根据降噪效果自动切换自适应与固定滤波模式,减少无效通信。
  • 通信时传输累积梯度差值,仅需少量数据即可恢复性能。
  • 适合对稳定性要求高的工业级分布式降噪系统使用。

分布式多通道主动降噪(DMCANC)系统将传统集中式算法的高计算负载分摊至多个处理节点,通过节点间通信协同抑制噪声。然而,通信开销可能破坏算法稳定性并降低整体性能。为此,本文提出一种融合自适应-固定滤波切换与混合梯度组合策略的鲁棒通信框架。各节点独立执行单通道滤波参考最小均方(FxLMS)算法,并实时监测降噪效果。当当前降噪性能劣于先前状态时,节点停止自适应更新,切换至固定滤波,并主动发起通信请求。交换内容为当前控制滤波器与上次通信时滤波器的差值,等价于非通信时段的累积梯度和。接收邻近节点的累积梯度后,节点采用混合梯度组合方法更新滤波器,随后恢复自适应模式。该主动通信策略与自适应-固定切换机制有效缓解了通信问题带来的不稳定性风险。仿真表明,所提方法在通信受限条件下仍能实现与集中式算法相当的降噪性能,具备在实际分布式降噪场景中的应用潜力。

原文摘要 · Abstract (English)

Distributed multichannel active noise control (DMCANC) systems assign the high computational load of conventional centralized algorithms across multiple processing nodes, leveraging inter-node communication to collaboratively suppress unwanted noise. However, communication overhead can undermine algorithmic stability and degrade overall performance. To address this challenge, we propose a robust communication framework that integrates adaptive-fixed-filter switching and the mixed-gradient combination strategy. In this approach, each node independently executes a single-channel filtered reference least mean square (FxLMS) algorithm while monitoring real-time noise reduction levels. When the current noise reduction performance degrades compared to the previous state, the node halts its adaptive algorithm, switches to a fixed filter, and simultaneously initiates a communication request. The exchanged information comprises the difference between the current control filter and the filter at the time of the last communication, equivalent to the accumulated gradient sum during non-communication intervals. Upon receiving neighboring cumulative gradients, the node employs a mixed-gradient combination method to update its control filter, subsequently reverting to the adaptive mode. This proactive communication strategy and adaptive-fixed switching mechanism ensure system robustness by mitigating instability risks caused by communication issues. Simulations demonstrate that the proposed method achieves noise reduction performance comparable to centralized algorithms while maintaining stability under communication constraints, highlighting its practical applicability in real-world distributed ANC scenarios.

主动降噪分布式系统自适应滤波

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