Robust Recursive State Estimation with Random Measurements Droppings

Abstract: 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 the 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, which plays important roles in analyzing its asymptotic properties. Based on a Riemannian metric for positive definite matrices, some necessary and sufficient conditions have been obtained for the strict contractiveness of an iteration 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 gain matrix of this recursive robust state estimator converges in probability one to a stationary distribution. Numerical simulation results show that estimation accuracy of the suggested procedure is more robust against parametric modelling errors than the Kalman filter.