NDLI: Scalar quantizers for decentralized estimation of multiple random sources

Content Provider	IEEE Xplore Digital Library
Author	Vosoughi, A. Ren Gang
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Rochester, Rochester, NY (Vosoughi, A.; Ren Gang)
Abstract	We consider a new inference-centric application for a distributed sensor network. Consider multiple signal sources, i.e., acoustic sources, in the field being monitored. Governed by physics law each sensorpsilas measurement can be modeled as a linear combination of the original multiple signal sources, corrupted by the additive measurement noise. In a non-cooperative communication scenario, each sensor transmits (a summary of) its own observations to the fusion center (FC), that is interested in reconstruction of all the original signal sources. We show that the inherent correlation among sensorspsila measurements, which is due to their spatial proximity, can be effectively exploited to compress sensorspsila data and reduce the transmission rate, without necessarily comprising the inference performance, i.e., quality of the reconstructed sources at the FC. In particular, we propose a practically simple and yet effective encoding algorithm for sensors, built on the concept of distributed source coding, two data reconstruction schemes for the FC (referred to as pairing and sequential schemes), and two corresponding rate allocation policies. The proposed compression algorithm is developed based on the side information coding technique in. We further investigate the trade off between rate and source reconstruction quality for the proposed compression/reconstruction schemes and verify their effectiveness via simulations.
Starting Page	1
Ending Page	7
File Size	373176
Page Count	7
File Format	PDF
ISBN	9781424426768
DOI	10.1109/MILCOM.2008.4753286
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Acoustic sensors Acoustic measurements Noise measurement Additive noise Monitoring Physics Acoustic noise Sensor fusion Particle measurements Encoding
Content Type	Text
Resource Type	Article

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