A fuzzy adaptive extended Kalman filter exploiting the Student's t distribution for mobile robot tracking

To solve the problem of non-Gaussian distribution of measurement noise during the actual process of trajectory tracking when the mobile robot is performing tasks, a novel fuzzy adaptive extended Kalman filter exploiting the Student's t distribution for a robot path tracking is proposed. The distributions of process and measurement noise are modeled using the Student's t distribution. With the adaptive fuzzy controller, the adaptive factors are designed to adjust the covariance matrices of the process and measurement noises simultaneously, which optimize the posterior state and tracking accuracy. The simulation results show that the proposed algorithm has better accuracy and is more robust than existing state-of-the-art algorithms.