NDLI: Observability of a Linear System Under Sparsity Constraints

Content Provider	IEEE Xplore Digital Library
Author	Wei Dai Yuksel, S.
Copyright Year	1963
Abstract	Consider an n-dimensional linear system where it is known that there are at most nonzero components in the initial state. The observability problem, that is the recovery of the initial state, for such a system is considered. We obtain sufficient conditions on the number of available observations to be able to recover the initial state exactly for such a system. Both deterministic and stochastic setups are considered for system dynamics. In the former setting, the system matrices are known deterministically, whereas in the latter setting, all of the matrices are picked from a randomized class of matrices. The main message is that one does not need to obtain full n observations to be able to uniquely identify the initial state of the linear system, even when the observations are picked randomly, when the initial condition is known to be sparse.
Sponsorship	IEEE Control Systems Society
Starting Page	2372
Ending Page	2376
Page Count	5
File Size	1397464
File Format	PDF
ISSN	00189286
Volume Number	58
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Observability Vectors Linear systems Matrix decomposition Stochastic processes Sparse matrices Standards stochastic systems observability
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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