This paper proposes the consistent extended Kalman filter (CEKF) for the maneuver ing target tracking (MTT) with nonlinear uncertain dynamics, and applies it on hand position tracking. The general model of the MTT system is presented with unmodelled 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 online achieved. Furthermore, a CEKF based MTT algorithm is constructed via the tuning law of the critical parameter matrix Qk. 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 calcu lated by algorithms. All MSEs of CEFK 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 percentage of CEKF and EKF are 87.86% and 14.58%, as well as 97.41% and 41.79% in the experiments, respectively.