Application of the Three State Kalman Filtering for Moving Vehicle Tracking
Roberto Olivera, Reynel Olivera, Osbaldo Vite, Hamurabi Gamboa-Rosales, Miguel Angel Navarrete, Claudia Angelica Rivera-Romero · IEEE Latin America Transactions · 2016
The three-state Kalman filter (KF) is applied in the optimal estimation of three state (position, velocity and acceleration) in a moving vehicle; the problem is modeled like linear time invariant (LTI) system in presence of additive white Gaussian noise (AWGN). The steady-state filter parameters have been simulated and analyzed for different process acceleration noise (covariance). We show that KF estimation produce minimum mean square error (MSE) if acceleration noise and measurement noise are lower.