Hybrid GA-PSO Based Tuning of Unscented Kalman Filter for Bearings Only Tracking

The basic problem in Target tracking is to estimate the trajectory of a object from noise corrupted measurements and hence becoming very important field of research as it has wider applications in defense as well as civilian applications. Kalman filter is generally used for such applications. When the process and measurements are non linear extensions of Kalman filters like Extended Kalman Filter, Unscented Kalman Filters are widely used. UKF can give estimations up to second order characteristics of random process. Implementation of Kalman filter for any application is difficult because of initialization of Kalman filter i,e tuning of filter has to be performed before applying to real time situations. It demands prior estimations of Noise covariance matrices which are left for engineering intuitions. This paper presents the nonlinear state estimation using unscented Kalman filter and tuning of the filter is done using bio-inspired hybrid algorithms like PSO GA and Hybrid GA-PSO.