Amended Kalman Filtering with Intermittent Measurements in Target Tracking.

This paper focuses on the state estimation problem of target tracking with intermittent measurements. Leveraged by the posterior measurements, an amended Kalman filter is proposed in this paper to improve the precision of the current estimated state. Both the deduction and proof of the amended Kalman filter are discussed specifically to distinguish amended Kalman filter from the Kalman smoother. Extensive simulations are conducted and the simulation results verify the excellent tracking performance of the amended Kalman filter.