Maximum likelihood estimation of transition probabilities of jump Markov linear systems

Orguner U., Demirekler M.

IEEE TRANSACTIONS ON SIGNAL PROCESSING, cilt.56, sa.10, ss.5093-5108, 2008 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 56 Sayı: 10
  • Basım Tarihi: 2008
  • Doi Numarası: 10.1109/tsp.2008.928936
  • Dergi Adı: IEEE TRANSACTIONS ON SIGNAL PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.5093-5108
  • Anahtar Kelimeler: interacting multiple model (IMM), jump Markov linear system (JMLS), maximum likelihood (ML), transition probability matrix (TPM), STATE ESTIMATION, DISCRETE-TIME, ADAPTIVE ESTIMATION, PARTICLE FILTER, ALGORITHMS, MODELS, STATISTICS, PARAMETERS
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.