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This paper introduces a novel methodology for the identification of switchingdynamics for switched autoregressive linear models. Switching behavior isassumed to follow a Markov model. The system's outputs are contaminated bypossibly large values of measurement noise. Although the procedure provided canhandle other noise distributions, for simplicity, it is assumed that thedistribution is Normal with unknown variance. Given noisy input-output data, weaim at identifying switched system coefficients, parameters of the noisedistribution, dynamics of switching and probability transition matrix ofMarkovian model. System dynamics are estimated using previous results whichexploit algebraic constraints that system trajectories have to satisfy.Switching dynamics are computed with solving a maximum likelihood estimationproblem. The efficiency of proposed approach is shown with several academicexamples. Although the noise to output ratio can be high, the method is shownto be extremely effective in the situations where a large number ofmeasurements is available.

Identification of Markov Jump Autoregressive Processes from Large Noisy Data Sets