Networked Estimation Using Sparsifying Basis Prediction
2013 (English)In: IFAC Proceedings Volumes, 2013, 174-181 p.Conference paper (Refereed)
We present a framework for networked state estimation, where systems encode their (possibly high dimensional) state vectors using a mutually agreed basis between the system and the estimator (in a remote monitoring unit). The basis sparsifies the state vectors, i.e., it represents them using vectors with few non-zero components, and as a result, the systems might need to transmit only a fraction of the original information to be able to recover the non-zero components of the transformed state vector. Hence, the estimator can recover the state vector of the system from an under-determined linear set of equations. We use a greedy search algorithm to calculate the sparsifying basis. Then, we present an upper bound for the estimation error. Finally, we demonstrate the results on a numerical example.
Place, publisher, year, edition, pages
2013. 174-181 p.
, IFAC Proceedings Volumes (IFAC-PapersOnline), ISSN 1474-6670 ; Vol. 4, Issue PART 1
Networked Estimation, System state estimation, State monitoring, Sparsifying basis, Uncertain linear systems
Control Engineering Communication Systems
IdentifiersURN: urn:nbn:se:kth:diva-138591DOI: 10.3182/20130925-2-DE-4044.00050ScopusID: 2-s2.0-84886608411ISBN: 978-390282355-7OAI: oai:DiVA.org:kth-138591DiVA: diva2:681484
4th IFAC Workshop on Distributed Estimation and Control in Networked Systems, NecSys 2013; Koblenz, Germany, 25-26 September 2013
QC 201401162013-12-202013-12-202014-01-23Bibliographically approved