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Recursive Maximum Likelihood Identification of Jump Markov Nonlinear Systems
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
University of Cambridge, England.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2015 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 3, 754-765 p.Article in journal (Refereed) Published
Abstract [en]

We present an online method for joint state and parameter estimation in jump Markov non-linear systems (JMNLS). State inference is enabled via the use of particle filters which makes the method applicable to a wide range of non-linear models. To exploit the inherent structure of JMNLS, we design a Rao-Blackwellized particle filter (RBPF) where the discrete mode is marginalized out analytically. This results in an efficient implementation of the algorithm and reduces the estimation error variance. The proposed RBPF is then used to compute, recursively in time, smoothed estimates of complete data sufficient statistics. Together with the online expectation maximization algorithm, this enables recursive identification of unknown model parameters including the transition probability matrix. The method is also applicable to online identification of jump Markov linear systems(JMLS). The performance of the method is illustrated in simulations and on a localization problem in wireless networks using real data.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2015. Vol. 63, no 3, 754-765 p.
Keyword [en]
Adaptive filtering; expectation maximization; identification; jump Markov systems; parameter estimation; particle filter; Rao-Blackwellization; transition probability estimation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-114414DOI: 10.1109/TSP.2014.2385039ISI: 000348374000017OAI: oai:DiVA.org:liu-114414DiVA: diva2:791984
Note

Funding Agencies|Swedish Research Council under the Linnaeus Center (CADICS) Project Learning of complex dynamical systems [637-2014-466]; Frame Project Grant COOP-LOC; VR Project Scalable Kalman Filters

Available from: 2015-03-02 Created: 2015-02-20 Last updated: 2017-12-04

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Özkan, EmreFritsche, CarstenGustafsson, Fredrik

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