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Generalized Locally Most Powerful Tests for Distributed Sparse Signal Detection

Abstract

In this paper we tackle distributed detection of a localized phenomenon of interest (POI) whose signature is sparse via a wireless sensor network. We assume that both the position and the emitted power of the POI are unknown, other than the sparsity degree associated to its signature. We consider two communication scenarios in which sensors send either (i) their compressed observations or (ii) a 1-bit quantization of them to the fusion center (FC). In the latter case, we consider non-ideal reporting channels between the sensors and the FC. We derive generalized (i.e. based on Davies’ framework (Davies, 1977)) locally most powerful detectors for the considered problem with the aim of obtaining computationally-efficient fusion rules. Moreover, we obtain their asymptotic performance and, based on such result, we design the local quantization thresholds at the sensors by solving a 1-D optimization problem. Simulation results confirm the effectiveness of the proposed design and highlight only negligible performance loss with respect to counterparts based on the (more-complex) generalized likelihood ratio.

Category

Academic article

Language

English

Author(s)

Affiliation

  • Iran
  • University of Naples 'Federico II'
  • Norwegian University of Science and Technology
  • SINTEF Energy Research / Gassteknologi

Year

2022

Published in

IEEE Transactions on Signal and Information Processing over Networks

ISSN

2373-7778

Publisher

IEEE (Institute of Electrical and Electronics Engineers)

Volume

8

Page(s)

528 - 542

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