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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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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.