  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 extremely effective in the situations where a large number of
measurements is available.
