Maximum Likelihood Estimation of Transition Probabilities of Jump Markov Linear Systems
2008 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 10 II, 5093-5108 p.Article in journal (Refereed) Published
This paper describes an online maximum likelihood estimator for the transition probabilities associated with a jump Markov linear system (JMLS). The maximum likelihood estimator is derived using the reference probability method, which exploits an hypothetical probability measure to find recursions for complex expectations. Expectation maximization (EM) procedure is utilized for maximizing the likelihood function. In order to avoid the exponential increase in the number of statistics of the optimal EM algorithm, we make interacting multiple model (IMM)-type approximations. The resulting method needs the mode weights of an IMM filter with N3 components, where N is the number of models in the JMLS. The algorithm can also supply base-state estimates and covariances as a by-product. The performance of the estimator is illustrated on two simulated examples and compared to a recently proposed alternative.
Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2008. Vol. 56, no 10 II, 5093-5108 p.
Interacting multiple model (IMM), Jump Markov linear system (JMLS), Maximum likelihood (ML), Transition probability matrix (TPM)
IdentifiersURN: urn:nbn:se:liu:diva-49965DOI: 10.1109/TSP.2008.928936OAI: oai:DiVA.org:liu-49965DiVA: diva2:270861
© 2008 IEEE.2009-10-112009-10-112013-07-22