Title :
Information Bounds for Decentralized Sequential Detection
Author_Institution :
Sch. of Ind. & Syst. Eng., Georgia Inst. of Technol., Atlanta, GA
Abstract :
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
Keywords :
Gaussian noise; decision making; error statistics; heuristic programming; sensor fusion; statistical testing; additive Gaussian sensor noises; asymptotic theory; decentralized decision systems; decentralized sequential detection; error probabilities constraints; information bounds; optimal binary quantizers; Additive noise; Bayesian methods; Electronic mail; Error probability; Feedback; Sensor fusion; Sensor systems; Sequential analysis; System testing; Systems engineering and theory;
Conference_Titel :
Information Theory, 2006 IEEE International Symposium on
Conference_Location :
Seattle, WA
Print_ISBN :
1-4244-0505-X
Electronic_ISBN :
1-4244-0504-1
DOI :
10.1109/ISIT.2006.262133
