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.|
|Number of pages||5|
|State||Published - Aug 10 2016|
|Event||2016 IEEE International Symposium on Information Theory, ISIT 2016 - Barcelona, Spain|
Duration: Jul 10 2016 → Jul 15 2016
|Name||IEEE International Symposium on Information Theory - Proceedings|
|Other||2016 IEEE International Symposium on Information Theory, ISIT 2016|
|Period||7/10/16 → 7/15/16|
Bibliographical noteFunding Information:
Jingchen Liu is supported in part by Army Grant, W911NF-15-1-0159 and NSF, SES-1323977
© 2016 IEEE.
Copyright 2017 Elsevier B.V., All rights reserved.
- Sequential hypothesis testing
- dynamic programming
- finite horizon
- online sensor selection
- sensor usages