Hand Position Tracking based on Optimized Consistent Extended Kalman Filter

This paper proposes a hand position tracking algorithm based on optimized consistent extended Kalman filter (CEKF). By introducing the previous work of the authors and analyzing the parameter of the original CEKF algorithm, the key parameter that is negatively correlated with the degree of the estimation of uncertain dynamics is determined. Then, two metaheuristic methods, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) are used to optimize the original CEKF algorithm. To quantify the performance of the algorithms, the root-mean-square error (RMSE) is employed as the performance index. Finally, the numerical simulation and practical experiment of the hand position tracking are carried out, and the optimized algorithm achieves 9.52% and 10.94% improvements of the performance, respectively.