TY - JOUR
T1 - Development and application of an enhanced kalman filter and global positioning system error-correction approach for improved map-matching
AU - Xu, Hao
AU - Liu, Hongchao
AU - Tan, Chin Woo
AU - Bao, Yuanlu
N1 - Funding Information:
This research was supported in part by the National Natural Science Foundation of China Grant Number 60272040. The authors are grateful to Mrs. Kimberly D. Harris for her technical editing of this article.
PY - 2010/1
Y1 - 2010/1
N2 - 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.
AB - 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.
KW - Global Positioning System
KW - Kalman filter
KW - Map-matching
KW - Vehicle navigation system
UR - http://www.scopus.com/inward/record.url?scp=77149147434&partnerID=8YFLogxK
U2 - 10.1080/15472450903386013
DO - 10.1080/15472450903386013
M3 - Article
AN - SCOPUS:77149147434
SN - 1547-2450
VL - 14
SP - 27
EP - 36
JO - Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
JF - Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
IS - 1
ER -