Automatic Parameter Setting Method for an Accurate Kalman Filter Tracker Using an Analytical Steady-State Performance Index

Abstract

We present an automatic parameter setting method to achieve an accurate second-order Kalman filter tracker based on a steady-state performance index. First, we propose an efficient steady-state performance index that corresponds to the root-mean-square (rms) prediction error in tracking. We then derive an analytical relationship between the proposed performance index and the generalized error covariance matrix of the process noise, for which the automatic determination using the derived relationship is presented. The model calculated by the proposed method achieves better accuracy than the conventional empirical model of process noise. Numerical analysis and simulations demonstrate the effectiveness of the proposed method for targets with accelerating motion. The rms prediction error of the tracker designed by the proposed method is 63.8% of that with the conventional empirically selected model for a target accelerating at 10 m/s2.