State estimation of nonlinear dynamic system using novel heuristic filter based on genetic algorithm

This paper introduces a new filter for nonlinear systems state estimation. The new filter formulates the state estimation problem as a stochastic dynamic optimization problem and utilizes a new stochastic method based on genetic algorithm to find and track the best estimation. In the proposed filter, each individual is set based on stochastic selection and multiple mutations to find the best estimation at every time step. The population searches the state space dynamically in a similar scheme to the optimization algorithm. This approach is applied to estimate the state of some nonlinear dynamic systems with noisy measurement and its performance is compared with other filters. The results indicate an improved performance of heuristic filters relatives to classic versions. Comparison of the results to those of extend Kalman filter, unscented Kalman filter, particle filter and heuristic filters indicated that the proposed heuristic filter called genetic filter fulfills the essential requirements of fast and accuracy for nonlinear state estimation.