Segmentation of nonstationary signals

A very useful and not too restrictive class of models of nonstationary signals is based upon the assumptions that the signals are composed of independent and stationary segments that can be represented by autoregressive models. A usual task is then to find the number of segments of the observed signal, their boundaries, and the best model for each segment. A Bayesian solution to this task is proposed which does not require setting of any thresholds. The technical implementation of the solution is carried out via dynamic programming. The Monte Carlo simulations show excellent results.<
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