Fusion Kalman/UFIR Filter for State Estimation With Uncertain Parameters and Noise Statistics

Abstract

In this paper, we fuse the Kalman filter (KF) that is optimal but not robust with the unbiased finite-impulse response (UFIR) filter which is more robust than KF but not optimal. The fusion filter employs the KF and UFIR filter as subfilters and produces smaller errors under the industrial conditions. In order to provide the best fusion effect, the operation point where UFIR meets Kalman is determined by applying probabilistic weights to each subfilter. Extensive simulations of the three degree of freedom (3-DOF) hover system have shown that the fusion filter output tends to range close to that by the best subfilter. Experimental verification provided for a 1-DOF torsion system has confirmed validity of simulation.