Aplicación del Filtro Kalman de Tres Estados en el Rastreo de un Vehículo en Movimiento (Application of the Three State Kalman Filtering for Moving Vehicle Tracking)

Roberto Olivera (roliverar@uaz.edu.mx)1, Reynel Olivera (reynel@uaz.edu.mx)1, Osbaldo Vite (osvichz@uaz.edu.mx)1, Hamurabi Gamboa (hamurabigr@uaz.edu.mx)1, Miguel Ángel Navarrete (mccnavarrete@gmail.com)1, Claudia Angélica Rivera (c.a.riveraromero@alumnos.uaslp.edu.mx)2


1Universidad Autónoma de Zacatecas
2Universidad Autónoma de San Luis Potosí

This paper appears in: Revista IEEE América Latina

Publication Date: May 2016
Volume: 14,   Issue: 5 
ISSN: 1548-0992


Abstract:
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.

Index Terms:
LTI systems, optimal estimation, Kalman filter, mean square error, steady-state   


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