LAUSR

This study present the design of extended Kalman filter (EKF) for object position tracking. It is required to accurately track the position of an object amidst noisy measurements. The state variables and nonlinear output equations were obtained for a flying object at a fixed point position. An extended Kalman filter and its algorithm was developed in the embedded Matlab/Simulink function block. The measurement noise was introduced in the filter using the random noise block of the Matlab/Simulink block code. Simulations were performed at 0.1s sampling intervals. The output error standard deviation was varied between 0.05 and 1. This results to optimal selection of the system noise covariance matrix. The simulation results obtained showed that the designed extended Kalman filter accurately tracked object position with improved filtering performance by effective tuning of the noise covariance matrix.