Distributed Detection of Sparse Signals with Censoring Sensors Via Locally Most Powerful Test

Chengxi Li, Gang Li, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


In this letter, we consider the problem of distributed detection of stochastic sparse signals in battery-powered sensor networks (SNs). For this problem, an original locally most powerful test (oLMPT) detector has previously been developed, where compressed measurements are collected from all local sensors and then fused at the fusion center (FC) for making the global decision. However, since the sensors always operate on limited energy resources, allowing all the nodes to send their observations to the FC all the time exerts tremendous pressure on their energy consumption and hinders the longevity of the sensors. To solve this problem, we propose a new censoring LMPT (cen-LMPT) detector by combining the strengths of censoring strategy and the oLMPT detector, where sensors are designated to merely send observations deemed informative enough so as to utilize the local energy more efficiently, and the FC still makes the global decision based on LMPT. We analytically derive the relationship between the detection performance and the communication rate for the proposed detector. It is shown that, compared with the oLMPT detector, the proposed cen-LMPT detector with the same number of nodes can achieve almost the same detection performance with significantly lower communication rate and, therefore, much lower local energy consumption. The simulation results verify our theoretical findings.

Original languageEnglish (US)
Article number8976280
Pages (from-to)346-350
Number of pages5
JournalIEEE Signal Processing Letters
StatePublished - 2020
Externally publishedYes


  • Censoring sensors
  • distributed detection
  • energy constraints
  • locally most powerful tests
  • sparse signals

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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