Dependent Randomization in Parallel Binary Decision Fusion

Weiqiang Dong, Moshe Kam

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


We consider a parallel decentralized detection system employing a bank of local detectors (LDs) to access a commonly-observed phenomenon. The system makes a binary decision about the phenomenon, accepting one of two hypotheses (H_0 ('absent') or H_1 ('present')). The kth LD uses a local decision rule to compress its local observations y_k into a binary local decision u_k;\ u_k=0 if the kth LD accepts H_0 and u_k=1 if it accepts H_1. The kth LD sends its decision u_k over a noiseless dedicated channel to a Data Fusion Center (DFC). The DFC combines the local decisions it receives from n LDs (u_1, u_2,\ldots, u_n) into a single binary global decision u_0 (u_0=0 for accepting H_0 or u_0=1 for accepting H_1). If each LD uses a single deterministic local decision rule (calculating u_k from the local observations y_k) and the DFC uses a single deterministic global decision rule (calculating u_0 from the n local decisions), the team receiver operating characteristic (ROC) curve is in general non-concave. The system's performance under a Neyman-Pearson criterion may then be suboptimal in the sense that a mixed strategy may yield a higher probability of detection when the probability of false alarm is constrained not to exceed a certain value, \alpha > 0. Specifically, a 'dependent randomization' detection scheme can be applied in certain circumstances to improve the system's performance by making the ROC curve concave. This scheme requires a coordinated and synchronized action between the DFC and the LDs. In this study, we specify when dependent randomization is needed, and discuss the proper response of the detection system if synchronization between the LDs and the DFC is temporarily lost.

Original languageEnglish (US)
Article number9317693
Pages (from-to)361-376
Number of pages16
JournalIEEE/CAA Journal of Automatica Sinica
Issue number2
StatePublished - Feb 2021

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Artificial Intelligence
  • Information Systems
  • Control and Systems Engineering


  • Data fusion
  • decision fusion
  • dependent randomization
  • parallel decentralized detection
  • synchronization


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