Research Article
Improving Wireless Positioning with Look-ahead Map-Matching
@INPROCEEDINGS{10.1109/MOBIQ.2007.4450983, author={Kipp Jones and Ling Liu and Farshid Alizadeh-Shabdiz}, title={Improving Wireless Positioning with Look-ahead Map-Matching}, proceedings={4th International ICST Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services}, publisher={IEEE}, proceedings_a={MOBIQUITOUS}, year={2008}, month={2}, keywords={Access Points Location Based Services Map Matching WiFi Wireless Networks}, doi={10.1109/MOBIQ.2007.4450983} }- Kipp Jones
Ling Liu
Farshid Alizadeh-Shabdiz
Year: 2008
Improving Wireless Positioning with Look-ahead Map-Matching
MOBIQUITOUS
IEEE
DOI: 10.1109/MOBIQ.2007.4450983
Abstract
Determining the location of mobile devices is a necessary system function for a growing number of location based services (LBS). The most popular method for location determination is GPS, however GPS has known accuracy and environmental limitations, many of which are exacerbated in dense urban areas. Wireless Positioning Systems (WPS) on the other hand, demonstrate location accuracy largely inverted to that of GPS – the denser the urban setting, the more accurate the location estimation is in general. Large-scale WPS differs from satellite based positioning in at least two aspects; first, wireless positioning systems typically derive their location estimates based on observed beacon locations such as through wardriving, and second, WPS lacks a mechanism to maintain highly synchronized clocks. This results in lower accuracy and a lower confidence factor in the use of wireless positioning. This paper presents a location estimation method that improves location accuracy for WPS through the use of digital map-matching of wardriving data. We have conducted initial experiments to evaluate our map-matching algorithm along with the enhanced location estimation approach and demonstrate its effectiveness for measuring and improving the accuracy of large-scale wireless positioning systems. We demonstrate extensions to a look-ahead map-matching algorithm to improve the accuracy and motivate further research in the area of large-scale map matching.
