Quantization of Prior Probabilities for Collaborative Distributed Hypothesis Testing

被引：23

作者：

Rhim, Joong Bum ^{[1
,2
]}

Varshney, Lav R. ^{[3
]}

Goyal, Vivek K. ^{[1
,2
]}

机构：

[1] MIT, Dept Elect Engn & Comp Sci, Cambridge, MA 02139 USA

[2] MIT, Elect Res Lab, Cambridge, MA 02139 USA

[3] IBM Thomas J Watson Res Ctr, Hawthorne, NY USA

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2012年 / 60卷 / 09期

基金：

美国国家科学基金会;

关键词：

Bayesian hypothesis testing; Bregman divergence; mean Bayes risk minimization; quantization theory; team theory; ORDER-STATISTICS; THINKING; SAMPLES; SIGNAL;

D O I：

10.1109/TSP.2012.2200890

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper studies the quantization of prior probabilities, drawn from an ensemble, in distributed detection with data fusion by combination of binary local decisions. Design and performance equivalences between a team of N agents and a more powerful single agent are obtained. Effects of identical quantization and diverse quantization on mean Bayes risk are compared. It is shown that when agents using diverse quantizers interact to agree on a perceived common risk, the effective number quantization levels is increased. With K this collaboration, optimal diverse regular quantization with N(K-1)+1 cells per quantizer performs as well as optimal identical quantization with cells per quantizer. Similar results are obtained for the maximum Bayes risk error criterion.

引用

页码：4537 / 4550

页数：14

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