Fusing dependent decisions for hypothesis testing with heterogeneous sensors

Satish G. Iyengar, Ruixin Niu, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


In this paper, we consider a binary decentralized detection problem where the local sensor observations are quantized before their transmission to the fusion center. Sensor observations, and hence their quantized versions, may be heterogeneous as well as statistically dependent. A composite binary hypothesis testing problem is formulated, and a copula-based generalized likelihood ratio test (GLRT) based fusion rule is derived given that the local sensors are uniform multilevel quantizers. An alternative computationally efficient fusion rule is also designed which involves injecting a deliberate random disturbance to the local sensor decisions before fusion. Although the introduction of external noise causes a reduction in the received signal-to-noise ratio (SNR), it is shown that the proposed approach can result in a detection performance comparable to the GLRT detector without external noise, especially when the number of quantization levels is large.

Original languageEnglish (US)
Article number6210395
Pages (from-to)4888-4897
Number of pages10
JournalIEEE Transactions on Signal Processing
Issue number9
StatePublished - 2012


  • Copula theory
  • hypothesis testing
  • multimodal signals
  • multisensor fusion
  • quantization
  • statistical dependence
  • stochastic resonance

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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