分布式传感器网络中的声源定位

Thibaut Ajdler, I. Kozintsev, R. Lienhart, M. Vetterli
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引用次数: 66

摘要

本文研究了由具有音频I/O功能的移动通用计算和通信设备组成的分布式无线传感器网络中的声源定位问题。与基于专用麦克风阵列的众所周知的定位方法相比,在我们的设置中,声音定位是使用任意放置的传感器的稀疏阵列来执行的(在典型的场景中,定位是由房间内的几台笔记本电脑/ pda共同执行的)。因此,在这种情况下,任何远场假设都不再有效。此外,定位算法的性能还受到传感器位置不确定性和A/D同步误差的影响。本文提出的源定位算法分为两个步骤。在第一步中,对麦克风对的到达时间差(TDOAs)进行估计,在第二步中对源位置进行最大似然(ML)估计。我们评估了Cramer-Rao界(CRB)对位置估计方差的影响,并将其与仿真和实验结果进行了比较。我们还讨论了分布式阵列几何形状和传感器位置误差对定位算法性能的影响。系统的性能可能会受到传感器位置误差的限制,并且当麦克风相对于源具有较大孔径时,系统的性能会增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Acoustic source localization in distributed sensor networks
This paper studies the problem of sound source localization in a distributed wireless sensor network formed by mobile general purpose computing and communication devices with audio I/O capabilities. In contrast to well understood localization methods based on dedicated microphone arrays, in our setting sound localization is performed using a sparse array of arbitrary placed sensors (in a typical scenario, localization is performed by several laptops/PDAs co-located in a room). Therefore any far-field assumptions are no longer valid in this situation. Additionally, localization algorithm's performance is affected by uncertainties in sensor position and errors in A/D synchronization. The proposed source localization algorithm consists of two steps. In the first step, time differences of arrivals (TDOAs) are estimated for the microphone pairs, and in the second step the maximum likelihood (ML) estimation for the source position is performed. We evaluate the Cramer-Rao bound (CRB) on the variance of the location estimation and compare it with simulations and experimental results. We also discuss the effects of distributed array geometry and errors in sensor positions on the performance of the localization algorithm. The performances of the system are likely to be limited by errors in sensor locations and increase when the microphones have a large aperture with respect to the source.
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