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Optimal State Estimation with Measurements Corrupted by Laplace Noise
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.
2016 (English)In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, 302-307 p., 7798286Conference paper (Refereed)
Abstract [en]

Optimal state estimation for linear discrete-time systems is considered. Motivated by the literature on differential privacy, the measurements are assumed to be corrupted by Laplace noise. The optimal least mean square error estimate of the state is approximated using a randomized method. The method relies on that the Laplace noise can be rewritten as Gaussian noise scaled by Rayleigh random variable. The probability of the event that the distance between the approximation and the best estimate is smaller than a constant is determined as function of the number of parallel Kalman filters that is used in the randomized method. This estimator is then compared with the optimal linear estimator, the maximum a posteriori (MAP) estimate of the state, and the particle filter.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. 302-307 p., 7798286
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-208619DOI: 10.1109/CDC.2016.7798286ISI: 000400048100050ScopusID: 2-s2.0-85010711687ISBN: 978-1-5090-1837-6 OAI: oai:DiVA.org:kth-208619DiVA: diva2:1108035
Conference
55th IEEE Conference on Decision and Control, CDC 2016, ARIA Resort and CasinoLas Vegas, United States, 12 December 2016 through 14 December 2016
Funder
Swedish Civil Contingencies Agency
Note

QC 20170612

Available from: 2017-06-12 Created: 2017-06-12 Last updated: 2017-06-12Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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