Identification of Markov jump autoregressive processes from large noisy data sets

Sarah Hojjatinia, Constantino Lagoa

Research output: Contribution to journalConference articlepeer-review


This paper introduces a novel methodology for the identification of switching dynamics for switched autoregressive linear models. Switching behavior is assumed to follow a Markov model. The system's outputs are contaminated by possibly large values of measurement noise. Although the procedure provided can handle other noise distributions, for simplicity, it is assumed that the distribution is Normal with unknown variance. Given noisy input-output data, we aim at identifying switched system coefficients, parameters of the noise distribution, dynamics of switching and probability transition matrix of Markovian model. System dynamics are estimated using previous results which exploit algebraic constraints that system trajectories have to satisfy. Switching dynamics are computed with solving a maximum likelihood estimation problem. The efficiency of proposed approach is shown with several academic examples. Although the noise to output ratio can be high, the method is shown to be effective in the situations where a large number of measurements is available.

Original languageEnglish (US)
Pages (from-to)1077-1083
Number of pages7
Issue number2
StatePublished - 2020
Event21st IFAC World Congress 2020 - Berlin, Germany
Duration: Jul 12 2020Jul 17 2020

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


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