Encyclopedia of Systems and Control

Living Edition
| Editors: John Baillieul, Tariq Samad

Particle Filters

Living reference work entry
DOI: https://doi.org/10.1007/978-1-4471-5102-9_66-1

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.

Keywords

Nonlinear filtering Sequential Monte Carlo Estimation Kalman filter 
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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Division of Automatic Control, Department of Electrical Engineering, Linköping UniversityLinköpingSweden