Recursive Maximum Likelihood Identification of Jump Markov Nonlinear Systems

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2015-02-01

Author

Özkan, Emre
Fritsche, Carsten
Gustafsson, Fredrik

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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.

Subject Keywords

Adaptive filtering, Particle filter, Rao-Blackwellization, Expectation maximization, Parameter estimation, Jump Markov Systems

URI

https://hdl.handle.net/11511/36013

Journal

IEEE TRANSACTIONS ON SIGNAL PROCESSING

DOI

https://doi.org/10.1109/tsp.2014.2385039

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

E. Özkan, C. Fritsche, and F. Gustafsson, “Recursive Maximum Likelihood Identification of Jump Markov Nonlinear Systems,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, pp. 754–765, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36013.