无线声学传感器和执行器网络中节点特定声音分区的分布式自适应声学对比控制

Robbe Van Rompaey, M. Moonen
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引用次数: 2

摘要

本文提出了一种基于全网声学对比控制(ACC)方法的无线声学传感器和执行器网络(WASAN)中节点特定声音分区的分布式自适应算法。ACC方法的目标是同时创建具有高信号功率的节点特定区域(亮区),同时最小化其他节点特定区域(暗区)的功率泄漏。为此,提出了一个涉及WASAN中所有扬声器和麦克风之间声学耦合的全网目标,其中最优解基于集中广义特征值分解(GEVD)。为了允许分布式处理,首先提出了一种基于梯度的GEVD算法,该算法最小化了相同的目标。然后可以修改该算法以允许完全分布式的实现,包括网络内求和和简单的本地处理。该算法称为基于分布式自适应梯度的ACC算法(DAGACC)。计算机仿真结果表明,该算法仅经过几次迭代就优于非合作分布式解决方案,并收敛于集中式解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed Adaptive Acoustic Contrast Control for Node-specific Sound Zoning in a Wireless Acoustic Sensor and Actuator Network
This paper presents a distributed adaptive algorithm for node-specific sound zoning in a wireless acoustic sensor and actuator network (WASAN), based on a network-wide acoustic contrast control (ACC) method. The goal of the ACC method is to simultaneously create node-specific zones with high signal power (bright zones) while minimizing power leakage in other node-specific zones (dark zones). To obtain this, a network-wide objective involving the acoustic coupling between all the loudspeakers and microphones in the WASAN is proposed where the optimal solution is based on a centralized generalized eigenvalue decomposition (GEVD). To allow for distributed processing, a gradient based GEVD algorithm is first proposed that minimizes the same objective. This algorithm can then be modified to allow for a fully distributed implementation, involving in-network summations and simple local processing. The algorithm is referred to as the distributed adaptive gradient based ACC algorithm (DAGACC). The proposed algorithm outperforms the non-cooperative distributed solution after only a few iterations and converges to the centralized solution, as illustrated by computer simulations.
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