Robust detection of random events with spatially correlated data in wireless sensor networks via distributed compressive sensing

Thakshila Wimalajeewa, Pramod Kumar Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we exploit the theory of compressive sensing to perform detection of a random source in a dense sensor network. When the sensors are densely deployed, observations at adjacent sensors are highly correlated while those corresponding to distant sensors are less correlated. Thus, the covariance matrix of the concatenated observation vector of all the sensors at any given time can be sparse where the sparse structure depends on the network topology and the correlation model. Exploiting the sparsity structure of the covariance matrix, we develop a robust nonparametric detector to detect the presence of the random event using a compressed version of the data collected at the distributed nodes. We employ the multiple access channel (MAC) model with distributed random projections for sensors to transmit observations so that a compressed version of the observations is available at the fusion center. Detection is performed by constructing a decision statistic based on the covariance information of uncompressed data which is estimated using compressed data. The proposed approach does not require any knowledge of the noise parameter to set the threshold, and is also robust when the distributed random projection matrices become sparse.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
Volume2017-December
ISBN (Electronic)9781538612514
DOIs
StatePublished - Mar 9 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao
Duration: Dec 10 2017Dec 13 2017

Other

Other7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period12/10/1712/13/17

Keywords

  • Compressive sensing
  • detection theory
  • random events
  • statistical dependence
  • wireless sensor networks

ASJC Scopus subject areas

  • Signal Processing
  • Control and Optimization
  • Instrumentation

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