Development and application of an enhanced kalman filter and global positioning system error-correction approach for improved map-matching

Hao Xu, Hongchao Liu, Chin Woo Tan, Yuanlu Bao

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Map-matching, which reconciles a vehicle's location with the underlying road map, is a fundamental function of a land vehicle navigation system. This article presents an improved Kalman filter approach whose state-space model is different from the conventional ones. The main objective of the research is to develop and apply a proper Kalman filter-based model for effectively correcting Global Positioning System (GPS) errors in map-matching. Based on the in-depth investigation of the characteristics of GPS errors, the authors presents a novel approach to update the state vector and other related parameters of the Kalman filter using both the historical tracks and road map information. The performance of the proposed approach is thoroughly examined by sample applications with real field data. The result shows that it handles the biased error and the random error of the GPS signals reasonably well in both the along-road and cross-road directions.

Original languageEnglish
Pages (from-to)27-36
Number of pages10
JournalJournal of Intelligent Transportation Systems: Technology, Planning, and Operations
Volume14
Issue number1
DOIs
StatePublished - Jan 2010

Keywords

  • Global Positioning System
  • Kalman filter
  • Map-matching
  • Vehicle navigation system

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