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Estimating time-varying parameters by the Kalman filter based algorithm: stability and convergence

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1 Author(s)
Guo, L. ; Dept. of Syst. Eng., Australian Nat. Univ., Canberra, ACT, Australia

Convergence and stability properties of the Kalman filter-based parameter estimator are established for linear stochastic time-varying regression models. The main features are: both the variances and sample path averages of the parameter tracking error are shown to be bounded; the regression vector includes both stochastic and deterministic signals, and no assumptions of stationarity or independence are requires; and the unknown parameters are only assumed to have bounded variations in an average sense

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Automatic Control, IEEE Transactions on  (Volume:35 ,  Issue: 2 )