Performance analysis of Kalman filter, fuzzy Kalman filter and wind driven optimized Kalman filter for tracking applications

State space estimation approaches are one of the finest approaches to predict the future behavior of real time systems like tracking, navigation, guidance systems etc. Kalman filter is employed for the estimation of system dynamics. It is very important to tune the parameters observation noise (measurement noise) and plant noise (process noise) for better estimation of dynamics of the system especially in tracking applications. Tuning of measurement noise and plant noise can be done through Nature Inspired Optimization techniques. Fuzzy logic is another approach to tune these parameters. In this paper the authors tried to estimate target motion parameters using Kalman filter and the performance is analyzed using the tuning methods Wind Driven Optimization (WDO) and fuzzy logic.