Identification of jump Markov linear models using particle filters
2014 (English)In: Proc. 53rd Conference on Decision and Control, Piscataway, NJ: IEEE , 2014Conference paper (Refereed)
Jump Markov linear models consists of a finite number of linear state space models and a discrete variable encoding the jumps (or switches) between the different linear models. Identifying jump Markov linear models makes for a challenging problem lacking an analytical solution. We derive a new expectation maximization (EM) type algorithm that produce maximum likelihood estimates of the model parameters. Our development hinges upon recent progress in combining particle filters with Markov chain Monte Carlo methods in solving the nonlinear state smoothing problem inherent in the EM formulation. Key to our development is that we exploit a conditionally linear Gaussian substructure in the model, allowing for an efficient algorithm.
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE , 2014.
Signal Processing Control Engineering Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:uu:diva-234396OAI: oai:DiVA.org:uu-234396DiVA: diva2:756540
CDC 2014, December 15-17, Los Angeles, CA
FunderSwedish Research Council, 621-2013-5524Swedish Research Council, 637-2014-466