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Information bounds and quickest change detection in decentralized decision systems

Mei, Yajun (2005) Information bounds and quickest change detection in decentralized decision systems. IEEE Transactions on Information Theory, 51 (7). pp. 2669-2681. ISSN 0018-9448. http://resolver.caltech.edu/CaltechAUTHORS:MEIieeetit05

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Abstract

The quickest change detection problem is studied in decentralized decision systems, where a set of sensors receive independent observations and send summary messages to the fusion center, which makes a final decision. In the system where the sensors do not have access to their past observations, the previously conjectured asymptotic optimality of a procedure with a monotone likelihood ratio quantizer (MLRQ) is proved. In the case of additive Gaussian sensor noise, if the signal-to-noise ratios (SNR) at some sensors are sufficiently high, this procedure can perform as well as the optimal centralized procedure that has access to all the sensor observations. Even if all SNRs are low, its detection delay will be at most pi/2-1 approximate to 57% larger than that of the optimal centralized procedure. Next, in the system where the sensors have full access to their past observations, the first asymptotically optimal procedure in the literature is developed. Surprisingly, the procedure has the same asymptotic performance as the optimal centralized procedure, although it may perform poorly in some practical situations because of slow asymptotic convergence. Finally, it is shown that neither past message information nor the feedback from the fusion center improves the asymptotic performance in the simplest model.


Item Type:Article
Additional Information:“© 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” Manuscript received November 21, 2002; revised November 10, 2004. This work was supported in part by the National Institutes of Health under Grant R01 AI055343. The material in this correspondence was presented in part at the IEEE International Symposium on Information Theory, Chicago, IL, June/July 2004. The author would like to thank his advisor Dr. Gary Lorden, for his constant support and encouragement, Dr. Venugopal V. Veeravalli for bringing this problem to his attention, as well as Dr. Alexander G. Tartakovsky for fruitful discussions. The author also would like to thank the referees for helpful suggestions, which led to significant improvements in organization and presentation.
Subject Keywords:asymptotic optimality, CUSUM, multisensor, quantization, sensor networks, sequential detection, stochastic-systems, fusion
Record Number:CaltechAUTHORS:MEIieeetit05
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:MEIieeetit05
Alternative URL:http://dx.doi.org/10.1109/TIT.2005.850159
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:865
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:31 Oct 2005
Last Modified:26 Dec 2012 08:41

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