Maximum likelihood estimation of transition probabilities of jump Markov linear systems

Date

2008-10-01

Author

Orguner, Umut

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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 N(3) 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.

Subject Keywords

Signal Processing, Electrical and Electronic Engineering

URI

https://hdl.handle.net/11511/42986

Journal

IEEE TRANSACTIONS ON SIGNAL PROCESSING

DOI

https://doi.org/10.1109/tsp.2008.928936

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

U. Orguner, “Maximum likelihood estimation of transition probabilities of jump Markov linear systems,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, pp. 5093–5108, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42986.