ABSTRACT
The Particle Filter Method is a sequential Monte Carlo technique for the solution of state estimation problems. The method was devised for the solution of nonlinear and/or non-Gaussian problems, for which the posterior distribution of the state variables is neither Gaussian nor analytical. Although several different versions of the Particle Filter method can be found in the literature, this chapter presents three algorithms that are at the same simple and robust. These three algorithms are presented in order of increasing complexity. They can cope with state estimation problems of practical interest as illustrated with examples in this chapter.
