A neyman-pearson type sensor censoring scheme for compressive distributed sparse signal recovery

Jwo-Yuh Wu, Ming Hsun Yang, Tsang Yi Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

To strike a balance between energy efficiency and data quality control, this paper proposes a Neyman-Pearson type sensor censoring scheme for distributed sparse signal recovery via compressive-sensing based on wireless sensor networks. In the proposed approach, each sensor node employs a sparse sensing vector with known support for data compression, meanwhile enabling making local inference about the unknown support of the sparse signal vector of interest. This naturally leads to a ternary censoring protocol, whereby each sensor (i) directly transmits the real-valued compressed data if the sensing vector support is detected to be overlapped with the signal support, (ii) sends a one-bit hard decision if empty support overlap is inferred, (iii) keeps silent if the measurement is judged to be uninformative. Our design then aims at minimizing the error probability that empty support overlap is decided but otherwise is true, subject to the constraints on a tolerable false-alarm probability that non-empty support overlap is decided but otherwise is true, and a target censoring rate. We derive a closed-form formula of the optimal censoring rule; a low complexity implementation using bi-section search is also developed. Computer simulations are used to illustrate the performance of the proposed scheme.

Original languageEnglish
Title of host publication2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
PublisherIEEE Computer Society
Pages213-217
Number of pages5
ISBN (Print)9781538647523
DOIs
StatePublished - 27 Aug 2018
Event10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018 - Sheffield, United Kingdom
Duration: 8 Jul 201811 Jul 2018

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
Volume2018-July
ISSN (Electronic)2151-870X

Conference

Conference10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
CountryUnited Kingdom
CitySheffield
Period8/07/1811/07/18

Keywords

  • Censoring
  • Compressive Sensing
  • Distributed Estimation
  • Energy Efficiency
  • Wireless Sensor Networks

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