Iterated extended Kalman filter for time-delay systems with multi-sample-rate measurements

Although optimal fusion algorithms of system state estimation have been proposed and studied for FAST in the past years, multi-sample-rate measurements and observation time-delay have always been restrictive factors to derive a glorious fusion results for state estimation. Based on the optimal fusion algorithm in the minimum mean square error sense, an iterated extended Kalman filter is investigated for discrete-time systems with multi-sample-rate measurements and delayed measurements in this paper, re-sampling observations from high sampling frequency channel and reducing the usage rate of observations with greater noises to investigate the estimation problem. The performance and improvement is clearly demonstrated through the numerical example. This study is manifestly advantageous for the feed supporting system of FAST.