Optimal sequential test with finite horizon and constrained sensor selection

Shang Li; Xiaoou Li; Xiaodong Wang; Jingchen Liu

doi:10.1109/ISIT.2016.7541558

Optimal sequential test with finite horizon and constrained sensor selection

Shang Li, Xiaoou Li, Xiaodong Wang, Jingchen Liu

Statistics (Twin Cities)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

This work considers the online sensor selection for the finite-horizon sequential hypothesis testing. In particular, at each step of the sequential test, the 'most informative' sensor is selected based on all the previous samples so that the expected sample size is minimized. In addition, certain sensors cannot be used more than their prescribed budgets on average. Under this setup, we show that the optimal sensor selection strategy is a time-variant function of the running hypothesis posterior, and the optimal test takes the form of a truncated sequential probability ratio test. Both of these operations can be obtained through a simplified version of dynamic programming. Numerical results demonstrate that the proposed online approach outperforms the existing offline approach to the order of magnitude.

Original language	English (US)
Title of host publication	Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1546-1550
Number of pages	5
ISBN (Electronic)	9781509018062
DOIs	https://doi.org/10.1109/ISIT.2016.7541558
State	Published - Aug 10 2016
Event	2016 IEEE International Symposium on Information Theory, ISIT 2016 - Barcelona, Spain Duration: Jul 10 2016 → Jul 15 2016

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
Volume	2016-August
ISSN (Print)	2157-8095

Other

Other	2016 IEEE International Symposium on Information Theory, ISIT 2016
Country/Territory	Spain
City	Barcelona
Period	7/10/16 → 7/15/16

Bibliographical note

Funding Information:
Jingchen Liu is supported in part by Army Grant, W911NF-15-1-0159 and NSF, SES-1323977

Publisher Copyright:
© 2016 IEEE.

Keywords

Sequential hypothesis testing
dynamic programming
finite horizon
online sensor selection
sensor usages

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10.1109/ISIT.2016.7541558

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Li, S., Li, X., Wang, X., & Liu, J. (2016). Optimal sequential test with finite horizon and constrained sensor selection. In Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory (pp. 1546-1550). Article 7541558 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2016-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2016.7541558

Optimal sequential test with finite horizon and constrained sensor selection. / Li, Shang; Li, Xiaoou; Wang, Xiaodong et al.
Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1546-1550 7541558 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2016-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Li, S, Li, X, Wang, X & Liu, J 2016, Optimal sequential test with finite horizon and constrained sensor selection. in Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory., 7541558, IEEE International Symposium on Information Theory - Proceedings, vol. 2016-August, Institute of Electrical and Electronics Engineers Inc., pp. 1546-1550, 2016 IEEE International Symposium on Information Theory, ISIT 2016, Barcelona, Spain, 7/10/16. https://doi.org/10.1109/ISIT.2016.7541558

Li S, Li X, Wang X, Liu J. Optimal sequential test with finite horizon and constrained sensor selection. In Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1546-1550. 7541558. (IEEE International Symposium on Information Theory - Proceedings). doi: 10.1109/ISIT.2016.7541558

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Optimal sequential test with finite horizon and constrained sensor selection

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Keywords

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