A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking

Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, 174-188.

Type

Journal article

Intellectual base

Review or monograph

Year

2002

Pages

174-188

DOI

10.1109/78.978374

DOI registrant

Institute of Electrical and Electronics Engineers

Link

https://doi.org/10.1109/78.978374

Cited by

2 citations across 2 artifacts.

Each citation links to the sentence it supports in the citing article.

Domain-specific

• Particle Filter
  • This weighted-population recursion is the stable family identity described by standard particle-filter tutorials and general sequential Monte Carlo treatments.

    This sourceDefines particle filters as sequential Monte Carlo point-mass representations, develops SIS/SIR and variants, and analyzes degeneracy and resampling.

• Recursive Bayesian estimation
  • A Bayes filter propagates the posterior through a transition model and multiplies by the new likelihood; Kalman, hidden Markov and particle filters are special computational forms under different assumptions.

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