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Bayesian Design of Tandem Networks for Distributed Detection With Multi-bit Sensor Decisions
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0003-1569-3527
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0001-6630-243X
2015 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 7, 1821-1831 p.Article in journal (Refereed) Published
Abstract [en]

We consider the problem of decentralized hypothesis testing under communication constraints in a topology where several peripheral nodes are arranged in tandem. Each node receives an observation and transmits a message to its successor, and the last node then decides which hypothesis is true. We assume that the observations at different nodes are, conditioned on the true hypothesis, independent and the channel between any two successive nodes is considered error-free but rate-constrained. We propose a cyclic numerical design algorithm for the design of nodes using a person-by-person methodology with the minimum expected error probability as a design criterion, where the number of communicated messages is not necessarily equal to the number of hypotheses. The number of peripheral nodes in the proposed method is in principle arbitrary and the information rate constraints are satisfied by quantizing the input of each node. The performance of the proposed method for different information rate constraints, in a binary hypothesis test, is compared to the optimum rate-one solution due to Swaszek and a method proposed by Cover, and it is shown numerically that increasing the channel rate can significantly enhance the performance of the tandem network. Simulation results for $M$-ary hypothesis tests also show that by increasing the channel rates the performance of the tandem network significantly improves.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2015. Vol. 63, no 7, 1821-1831 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing
URN: urn:nbn:se:kth:diva-159597DOI: 10.1109/TSP.2015.2401535ISI: 000350880900016ScopusID: 2-s2.0-84924674518OAI: diva2:786235

QC 201502010

Available from: 2015-02-05 Created: 2015-02-05 Last updated: 2015-04-13Bibliographically approved

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