NDLI: Decentralized Robust Acoustic Source Localization with Wireless Sensor Networks for Heavy-Tail Distributed Observations

Content Provider	IEEE Xplore Digital Library
Author	Yong Liu Yu Hen Hu Quan Pan
Copyright Year	2010
Abstract	In this work, an energy based acoustic source localization task in a wireless sensor network (WSN) is considered. Based on data gathered from field experiments, it is revealed that the acoustic energy gathered at sensor nodes exhibits a heavy-tail, non-Gaussian characteristic and should be fitted into a contaminated Gaussian model. This property renders conventional least square and maximum likelihood based location estimation methods ineffective. Leveraging the distributed, in-network processing nature of a WSN, a novel de-centralized robust acoustic source localization (DRASL) algorithm is proposed. With the DRASL, local sensor nodes receive sensor readings broadcast from neighboring sensors and independently compute local location estimates using a light-weight Iterative Nonlinear Reweighted Least Square (INRLS) algorithm. The local location estimate then will be relayed to a fusion center where the final location estimate is obtained as a weighted average of the local estimates. The potential advantage of this algorithm is validated using extensive simulation in a real-world operation scenario. It is show that its performance is superior than existing methods while promising to be more energy efficient.
Starting Page	1
Ending Page	5
File Size	313474
Page Count	5
File Format	PDF
ISBN	9781424456369
ISSN	1930529X
e-ISBN	9781424456383
e-ISBN	9781424456376
DOI	10.1109/GLOCOM.2010.5683474
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Acoustics Wireless sensor networks Peer to peer computing Noise Least squares approximation Iterative algorithm
Content Type	Text
Resource Type	Article

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