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Inverse Filtering for Linear Gaussian State-Space Models
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0003-0355-2663
Cornell Univ, Dept Elect & Comp Engn, Ithaca, NY 14853 USA.;Cornell Univ, Cornell Tech, Ithaca, NY 14853 USA..
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0002-1927-1690
2018 (English)In: 2018 IEEEĀ Conference on Decision and ControlĀ  (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 5556-5561, article id 8619013Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers inverse filtering problems for linear Gaussian state-space systems. We consider three problems of increasing generality in which the aim is to reconstruct the measurements and/or certain unknown sensor parameters, such as the observation likelihood, given posteriors (i. e., the sample path of mean and covariance). The paper is motivated by applications where one wishes to calibrate a Bayesian estimator based on remote observations of the posterior estimates, e. g., determine how accurate an adversary's sensors are. We propose inverse filtering algorithms and evaluate their robustness with respect to noise (e. g., measurement or quantization errors) in numerical simulations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 5556-5561, article id 8619013
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-245114DOI: 10.1109/CDC.2018.8619013ISI: 000458114805022Scopus ID: 2-s2.0-85062188998ISBN: 978-1-5386-1395-5 (print)OAI: oai:DiVA.org:kth-245114DiVA, id: diva2:1294123
Conference
57th IEEE Conference on Decision and Control, CDC 2018; Centre of the Fontainebleau in Miami Beac hMiami; United States; 17 December 2018 through 19 December 2018
Note

QC 20190306

Available from: 2019-03-06 Created: 2019-03-06 Last updated: 2019-03-06Bibliographically approved

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Mattila, RobertRojas, Cristian R.Wahlberg, Bo

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