Consistent Extended Kalman Filter Design for Maneuvering Target Tracking and Its Application on Hand Position Tracking

Abstract

This paper proposes the consistent extended Kalman filter (CEKF) for the maneuvering target tracking (MTT) with nonlinear uncertain dynamics, and applies it on hand position tracking. The general model of the MTT system is presented with unmodeled dynamics in terms of nonlinear unknown function of states. The CEKF is proposed to ensure that the bounds of the estimation error’s covariance matrix are available through the filter algorithm. As a result, the corresponding accuracy of the filter approach can be achieved online. Furthermore, a CEKF-based MTT algorithm is constructed via the tuning law of the critical parameter matrix [Formula: see text]. Finally, the effectiveness of CEKF is verified by MTT numerical simulations and hand tracking experiments under different maneuvers. Specifically, two indices are employed to compare the CEKF with extended Kalman filter (EKF): the mean square errors (MSEs) and the bounded percentage, i.e. the percentage of the range where the estimation error is enclosed by the bound calculated by algorithms. All MSEs of CEKF are smaller than those of EKF, where the worst MSEs of CEKF and EKF are 0.14 and 4.18 in the simulation, as well as 0.11 and 0.59 in the experiments, respectively; all bounded percentages of CEKF are larger than those of EKF, where the worst average bounded percentages of CEKF and EKF are 87.86% and 14.58%, as well as 97.41% and 41.79% in the experiments, respectively.