Distributed Detection of Sparse Stochastic Signals via Fusion of 1-bit Local Likelihood Ratios

Chengxi Li, You He, Xueqian Wang, Gang Li, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


In this letter, we consider the detection of sparse stochastic signals with sensor networks (SNs), where the fusion center (FC) collects 1-bit data from the local sensors and then performs global detection. For this problem, a newly developed 1-bit locally most powerful test (LMPT) detector requires 3.3Q sensors to asymptotically achieve the same detection performance as the centralized LMPT (cLMPT) detector with Q sensors. This 1-bit LMPT detector is based on 1-bit quantized observations without any additional processing at the local sensors. However, direct quantization of observations is not the most efficient processing strategy at the sensors since it incurs unnecessary information loss. In this letter, we propose an improved-1-bit LMPT (Im-1-bit LMPT) detector that fuses local 1-bit quantized likelihood ratios (LRs) instead of directly quantized local observations. In addition, we design the quantization thresholds at the local sensors to ensure asymptotically optimal detection performance of the proposed detector. It is shown theoretically and numerically that, with the designed quantization thresholds, the proposed Im-1-bit LMPT detector for the detection of sparse signals requires less number of sensor nodes to compensate for the performance loss caused by 1-bit quantization.

Original languageEnglish (US)
Article number8854811
Pages (from-to)1738-1742
Number of pages5
JournalIEEE Signal Processing Letters
Issue number12
StatePublished - Dec 2019
Externally publishedYes


  • 1-bit quantization
  • Distributed detection
  • locally most powerful tests
  • sparse signals

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering


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