A greedy algorithm for decentralized Bayesian detection with feedback

Document Type

Conference Proceeding

Publication Date

2-7-2017

Abstract

We consider a decentralized binary detection architecture comprised of n local detectors (LDs) communicating their decisions to a Data Fusion Center (DFC). Each local detector (LD) declares preference for one of two hypotheses (H0 or H1) and transmits it to the DFC. The decision of the kth LD at time step t is utk. The DFC develops a global preference ut0 for one of hypotheses based on the vector of local decisions Ut while minimizing a Bayesian cost. The input to the kth LD at time step t is the observation ytk collected from the surveyed environment, and the previous global decision ut-10. Alhakeem and Varshney developed a person-by-person optimal (PBPO) solution to this problem, namely a PBPO procedure to calculate the global fusion rule and the local decision rules. However, their solution requires that at each time step 2n fusion rule equations and 2n local threshold equations be solved simultaneously. In this paper we suggest a suboptimal solution to the problem, based on independent local minimizations of similar Bayesian cost by each LD and by the DFC. To assess the cost of decentralization, the performance of this solution is compared to that of an architecture that processes all observations in one central location.

Identifier

85015241586 (Scopus)

ISBN

[9781509015405]

Publication Title

37th IEEE Sarnoff Symposium Sarnoff 2016

External Full Text Location

https://doi.org/10.1109/SARNOF.2016.7846756

First Page

202

Last Page

207

Grant

N00014-13-1-0733

Fund Ref

Office of Naval Research

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