Abstract
State estimation is an important component for advance control and fault detection. This paper addresses the problem of smoother design for state estimation based on a finite number of measurements collected in a finite estimation horizon. Three different finite impulse response (FIR) smoothing algorithms are proposed using the maximum likelihood FIR estimation, which is robust against uncertain noise statistics and model parameters, and also independent of the initial states of each finite horizon. Moreover, we provide equivalent but iterative Kalman-like structures of these algorithms for practical implementation. The applications of the proposed smoothing algorithms to an object tracking and image processing examples are demonstrated, and it shows that they have better robustness against model uncertainties than traditional smoothing approaches.
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