Abstract
The particle filter computes a numeric approximation of the posterior distribution of the state trajectory in nonlinear filtering problems. This is done by generating random state trajectories and assigning a weight to them according to how well they predict the observations. The weights are instrumental in a resampling step, where trajectories are either kept or thrown away. This exposition will focus on explaining the main principles and the main theory in an intuitive way, illustrated with figures from a simple scalar example. A real-time application is used to graphically show how the particle filter solves a nontrivial nonlinear filtering problem.
This is a preview of subscription content, log in via an institution to check access.
Bibliography
Arulampalam S, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–188
Cappé O, Godsill SJ, Moulines E (2007) An overview of existing methods and recent advances in sequential Monte Carlo. IEEE Proc 95:899
Djuric PM, Kotecha JH, Zhang J, Huang Y, Ghirmai T, Bugallo MF, Miguez J (2003) Particle filtering. IEEE Signal Process Mag 20:19
Doucet A, de Freitas N, Gordon N (eds) (2001) Sequential Monte Carlo methods in practice. Springer, New York
Forssell U, Hall P, Ahlqvist S, Gustafsson F (2002) Novel map-aided positioning system. In: Proceedings of FISITA, Helsinki, number F02-1131
Gordon NJ, Salmond DJ, Smith AFM (1993) A novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc Radar Signal Process 140:107–113
Gustafsson F (2010) Particle filter theory and practice with positioning applications. IEEE Trans Aerosp Electron Mag Part II Tutor 7:53–82
Isard M, Blake A (1998) Condensation – conditional density propagation for visual tracking. Int J Comput Vis 29(1):5–28
Kitagawa G (1996) Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J Comput Graph Stat 5(1):1–25
Liu JS, Chen R (1998) Sequential Monte Carlo methods for dynamic systems. J Am Stat Assoc 93:1032–1044
Ristic B, Arulampalam S, Gordon N (2004) Beyond the Kalman filter: particle filters for tracking applications. Artech House, London
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag London
About this entry
Cite this entry
Gustafsson, F. (2013). Particle Filters. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_66-1
Download citation
DOI: https://doi.org/10.1007/978-1-4471-5102-9_66-1
Received:
Accepted:
Published:
Publisher Name: Springer, London
Online ISBN: 978-1-4471-5102-9
eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering