Comparison of hybrid HOD-GSA, HOD and PSO for the tuning of extended Kalman filter

Abstract

State estimation is the basic problem in every area of science and engineering. For the state estimation problem, Kalman filter is the generally used technique when the system is linear. Various derivatives of Kalman filter are proposed earlier for non-linear systems, i.e. Extended Kalman Filter and Unscented Kalman Filter. But, there is a need of tuning in these estimation techniques and therefore the tuning of process and measurement noise covariance matrices is required. Earlier, the different optimization techniques are used for the tuning of Extended Kalman Filter like Genetic Algorithm, Human Opinion Dynamics based Optimization and Particle Swarm Optimization. In this paper, Hybrid HOD-GSA has been proposed for the tuning of Extended Kalman Filter and also to solve the trapping problem of GSA. Then, the results taken from Hybrid HOD-GSA are compared with the results taken from Human Opinion Dynamics and Particle Swarm Optimization in terms of accuracy, error rate, standard deviation and convergence.