Design of an alpha-beta filter by combining fuzzy logic with evolutionary methods

The α-β filter based on the Kalman-like estimation scheme has been recognized as a outstanding tool for estimating the position and velocity signals of moving objects. Nevertheless, the performance of estimation heavily depends on the parameters α and β. In general, the choice of parameters is a trade-off optimization problem between the tracking accuracy and noise reduction capability. In order to obtain the suitable design of α-β filter for some specifications, a combined fuzzy logic and evolutionary optimization method is proposed for determining the parameter values. The simulation results are employed to illustrate the developed α-β filter which is capable of tracking the desired signals accurately and, at the same time, reducing the noise disturbance remarkably.