Data-driven power control for state estimation: a Bayesian inference approach

Title: Data-driven power control for state estimation: a Bayesian inference approach
Creator: Wu, Junfeng; Li, Yuzhe; Quevedo, Daniel E.; Lau, Vincent; Shi, Ling
Relation: Automatica Vol. 54, Issue April 2015, p. 332-339
Publisher Link: http://dx.doi.org/10.1016/j.automatica.2015.02.019
Publisher: Elsevier
Resource Type: journal article
Date: 2015
Description: We consider sensor transmission power control for state estimation, using a Bayesian inference approach. A sensor node sends its local state estimate to a remote estimator over an unreliable wireless communication channel with random data packet drops. As related to packet dropout rate, transmission power is chosen by the sensor based on the relative importance of the local state estimate. The proposed power controller is proved to preserve Gaussianity of local estimate innovation, which enables us to obtain a closed-form solution of the expected state estimation error covariance. Comparisons with alternative non-data-driven controllers demonstrate performance improvement using our approach.
Subject: Kalman filtering; transmission power control; state estimation; packet losses; Bayesian inference
Identifier: http://hdl.handle.net/1959.13/1338115
Identifier: uon:27973
Identifier: ISSN:0005-1098
Language: eng
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