Title :
State Variables Estimation Using Particle Filter: Experimental Comparison with Kalman Filter
Author :
del Toro Peral, M. ; Bravo, Fernando Gómez ; MartinhoVale, Alberto
Author_Institution :
Huelva Univ., Huelva
Abstract :
Within the probabilistic methods for the state estimation of a dynamic system, the particle filter approach is an innovative technique which is focusing the attention of current researches. Particle filtering succeeds in applying to different type of systems (linear and non-linear) and noise models. This paper presents a comparison between the results obtained using the particle Filter and the Kalman Filter for estimating the orientation and velocity of a DC motor. Real experiments are also presented.
Keywords :
DC motors; Kalman filters; machine control; probability; state estimation; velocity control; DC motor velocity; Kalman filter; particle filtering; probabilistic method; state variable estimation; Control systems; DC motors; Filtering; Gaussian noise; Kalman filters; Particle filters; Particle measurements; Sensor systems; State estimation; Uncertainty; Kalman Fikter; Particle Filter; State Estimation;
Conference_Titel :
Intelligent Signal Processing, 2007. WISP 2007. IEEE International Symposium on
Conference_Location :
Alcala de Henares
Print_ISBN :
978-1-4244-0829-0
Electronic_ISBN :
978-1-4244-0830-6
DOI :
10.1109/WISP.2007.4447543
