Application of Particle Filters to a Map-Matching Algorithm

Pavel Davidson; Jussi Collin; Jarmo Takala

doi:10.1134/S2075108711040067

Application of Particle Filters to a Map-Matching Algorithm

Pavel Davidson
, Jussi Collin
, Jarmo Takala

Research output: Contribution to journal › Article › Scientific › peer-review

22 Citations (Scopus)

1039 Downloads (Pure)

Abstract

This paper presents the numerical probabilistic approach to map-matching problem within the framework of Bayesian theory. The proposed solution is based on sequential Monte Carlo method, so called particle filtering. This algorithm can be adapted for implementation on real-time portable car navigation systems equipped with GPS or dead reckoning sensors. The algorithm reliability and accuracy performance was investigated using simulated data and data from real-world driving tests in urban environment.

Translated title of the contribution	Application of Particle Filters to a Map-Matching Algorithm
Original language	English
Pages (from-to)	285-292
Journal	Gyroscopy and Navigation
Volume	2
Issue number	4
DOIs	https://doi.org/10.1134/S2075108711040067
Publication status	Published - 2011
Publication type	A1 Journal article-refereed

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10.1134/S2075108711040067

Davidson application of particle filters
The original publication is available at http://www.springerlink.com/content/u6062037kj7jn80h/

http://urn.fi/URN:NBN:fi:tty-201201121007

Cite this

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AB - This paper presents the numerical probabilistic approach to map-matching problem within the framework of Bayesian theory. The proposed solution is based on sequential Monte Carlo method, so called particle filtering. This algorithm can be adapted for implementation on real-time portable car navigation systems equipped with GPS or dead reckoning sensors. The algorithm reliability and accuracy performance was investigated using simulated data and data from real-world driving tests in urban environment.

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Application of Particle Filters to a Map-Matching Algorithm

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