چكيده لاتين :
: In this article, sensor fusion for positioning a four wheeled autonomous vehicle, using indirect feedforward Kalman filter algorithm is simulated. Sensors such as accelerometer, tachometer, compass and potentiometer are used. At first, vehicle and its dynamic equations are introduced then system identification is done in order to find parameters of vehicle that is performed in two phases: vehicle parameters identification and motor parameters identification. Also, accelerometer noise and random bias reduction is done using direct and indirect Kalman filter. In order to remove the sensors error, indirect Kalman filter is utilized. The governing equations of error model are linear and time varying. Observability test indicates that these equations are not observable and Kalman filter implementation in such systems leads to non optimal result. A new approach for solving this problem based on finding the observable subsystem of the main system is introduced and Kalman filter is used for estimating the observable states. Simulation results show efficiency of the proposed method. Also, a new approach to find process and measurements noise covariance matrices using reverse shaping filter is presented.
