Particle filter

Summary

A particle filter is a Monte Carlo method that tracks the hidden state of a dynamical system from noisy observations. It represents the conditional distribution of the current state given the observations so far by a cloud of weighted samples, called particles. At each time step the particles are moved by the system dynamics, reweighted by how well they explain the new observation, and resampled. It works for nonlinear and non-Gaussian state-space models, such as hidden Markov models with continuous states, where exact filtering formulas are not available.