Robust Recursive State Estimation With Random Measurement Droppings

A recursive state estimation procedure is derived for a linear time varying system with both parametric uncertainties and stochastic measurement droppings. This estimator has a similar form as that of Kalman filter with intermittent observations, but its parameters should be adjusted when a plant output measurement arrives. A new recursive form is derived for the pseudo-covariance matrix of estimation errors. Based on a Riemannian metric for positive definite matrices, some necessary and sufficient conditions have been obtained for the strict contractiveness of this recursion. It has also been proved that under some controllability and observability conditions, as well as some weak requirements on measurement arrival probability, the update gain of this recursive robust state estimator and the mean of its squared estimation errors converge in probability one respectively to a corresponding stationary distribution. Numerical simulation results show that estimation accuracy of the suggested procedure is more robust against parametric modelling errors than Kalman filter.

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