State Variables Estimation Using Particle Filter: Experimental Comparison with Kalman Filter

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.