Abstract
The problem of robust real-time identification of linear single-input-single-output dynamic systems with stochastically time-varying parameters is considered. Two ways of constructing robust algorithms that are able to handle outliers contaminating the gaussian observation disturbance samples are discussed. The first way is based on the general formulation of dynamic stochastic approximation schemes characterized by an adequate non-linear residual transformation, as well as the step-by-step optimization with respect to the weighting matrix of the algorithm. The second way is based on the formulation of one-step optimal estimates. Monte Carlo simulation results illustrate the discussion and show the efficiency of the proposed algorithms in the presence of outliers.
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