Tuning of Extended Kalman Filter for nonlinear State Estimation

Kalman Filter is the most popular method for state estimation when the system is linear. State estimation is the typical issue in every part of engineering and science. But, for non linear systems, different extensions of Kalman Filter are used. Extended Kalman Filter is famous to discard the non linearity which uses First order Taylor series expansion. But for these estimation techniques, the tuning of process noise covariance and measurement noise covariance matrices is required. There are different optimization techniques used to tune the parameters of Extended Kalman Filter. In this paper, Particle Swarm Optimization has been proposed to tune the EKF parameters and then the simulations are implemented for permanent magnet synchronous motor.