Adaptive Kalman Filtering for Systems Subject to Randomly Delayed and Lost Measurements

Abstract

This paper investigates the problem of state estimation for discrete-time linear systems where the observation data are transmitted from the sensor to the filter subject to random delay and dropout. The loss and latency of the measurements are modeled by a group of Bernoulli distributed random variables with uncertain probabilities, which appear in the Kalman filter parameters. An adaptation factor, which is defined by comparing the theoretical and practical values of the innovation covariance, is employed to adjust the filter gains during estimation. Simulation results are presented to verify the improved performance of the proposed adaptive filter.