Information bounds for decentralized sequential detection

被引：0
|
作者：
Mei, Yajun [1 ]
机构：
[1] Georgia Inst Technol, Sch Ind & Syst Engn, Atlanta, GA 30329 USA
来源：
2006 IEEE International Symposium on Information Theory, Vols 1-6, Proceedings | 2006年
关键词：
D O I：
10.1109/ISIT.2006.262133
中图分类号：
TN [电子技术、通信技术];
学科分类号：
0809 ;
摘要：
The main purpose of this paper is to develop an asymptotic theory for the decentralized sequential hypothesis testing problems under the frequentist framework. Sharp asymptotic bounds on the average sample numbers or sample sizes of sequential or fixed-sample tests are provided in the decentralized decision systems in different scenarios subject to error probabilities constraints. Asymptotically optimal tests are offered in the system with full local memory. Optimal binary quantizers are also studied in the case of additive Gaussian sensor noises.
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页码：2647 / 2651
页数：5
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