Bayesian Sparse Signal Detection Exploiting Laplace Prior

Swatantra Kafle, Thakshila Wimalajeewa, Pramod Kumar Varshney

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

1 Scopus citations

Abstract

In this paper, we consider the problem of sparse signal detection with compressed measurements in a Bayesian framework. Multiple nodes in the network are assumed to observe sparse signals. Observations at each node are compressed via random projections and sent to a centralized fusion center. Motivated by the fact that reliable detection of the sparse signals does not require complete signal reconstruction, we propose two computationally efficient methods for constructing decision statistics for detection. First, using the Laplace prior directly to impose sparsity as widely considered in Bayesian Compressive Sensing (BCS), we develop an average likelihood ratio based detection method where the average is taken over the Laplace probability density function. Second, we exploit a three-stage hierarchical prior on the signal and construct decision statistics based on the noisy reconstruction (partial estimates) of the signals. Experimental results show that both average likelihood-based detection method and noisy-reconstruction based methods outperform most of the state-of-the-art algorithms.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4259-4263
Number of pages5
Volume2018-April
ISBN (Print)9781538646588
DOIs
StatePublished - Sep 10 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: Apr 15 2018Apr 20 2018

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period4/15/184/20/18

Keywords

  • Bayesian compressive sensing
  • Laplace prior
  • Multiple measurement vectors
  • Sparse signal detection
  • Tors

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Kafle, S., Wimalajeewa, T., & Varshney, P. K. (2018). Bayesian Sparse Signal Detection Exploiting Laplace Prior. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (Vol. 2018-April, pp. 4259-4263). [8461616] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8461616