NDLI: Optimal Periodic Sensor Scheduling in Networks of Dynamical Systems

Content Provider	IEEE Xplore Digital Library
Author	Sijia Liu Fardad, M. Varshney, P.K. Masazade, E.
Copyright Year	1991
Abstract	We consider the problem of finding optimal time-periodic sensor schedules for estimating the state of discrete-time dynamical systems. We assume that multiple sensors have been deployed and that the sensors are subject to resource constraints, which limits the number of times each can be activated over one period of the periodic schedule. We seek an algorithm that strikes a balance between estimation accuracy and total sensor activations over one period. We make a correspondence between active sensors and the nonzero columns of the estimator gain. We formulate an optimization problem in which we minimize the trace of the error covariance with respect to the estimator gain while simultaneously penalizing the number of nonzero columns of the estimator gain. This optimization problem is combinatorial in nature, and we employ the alternating direction method of multipliers (ADMM) to find its locally optimal solutions. Numerical results and comparisons with other sensor scheduling algorithms in the literature are provided to illustrate the effectiveness of our proposed method.
Sponsorship	IEEE Signal Processing Society
Starting Page	3055
Ending Page	3068
Page Count	14
File Size	3316812
File Format	PDF
ISSN	1053587X
Volume Number	62
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Schedules Frequency measurement Time measurement Scheduling Optimal scheduling Kalman filters state estimation Alternating direction method of multipliers dynamical systems optimization sensor networks sensor scheduling sparsity
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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