Improving Wireless Positioning with Look-ahead Map-Matching

學生 :張佑任老師 :劉宏煥老師

Jones, Kipp; Liu, Ling; Alizadeh-Shabdiz, Farshid;”Improving Wireless Positioning with Look-ahead Map-Matching”, The Fourth Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, P.1 – 8 , 6-10 Aug, 2007.

Reference

outline

• Introduction

• Method

• Algorithms

• Verification

• Conclusion

Introduction

• WiFi Positioning Services rely on the accuracy of WiFi beacons

• Use digital map to improve WiFi APs’ location estimation process

• Use map-matching to improving the estimation of the location of a vehical during scanning for access points

Method

• Wardriving the searching for Wi-Fi wireless networks by

moving vehicle. collect information about wireless access points.

• Map-matching compare the geographic coordinates of digital

map’s object against the GPS coordinates to constrain the GPS readings to point lie on the road

• LAMM

-refers to the ability to use future GPS readings to help idetify the current point inquestion

Algorithm

• Simple Distance Based Matching• Map-matching with look-ahead• Look-Ahead MM with Smothness Constraint

Simple map-matching problem

• Simple distance map matching

Map-matching with look-ahead

• Step1:fill buffer

• Step2:seed candidate tracks

• Step3:extend candidate tracks

• Step4:choose best set of candidate tracks

• Step5:get the next GPS readings

• Step6:find best tracks

• Step7:start next session and biging at step 1

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icandidate

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Basic look-ahead map-matching.

Look-ahead MM with Smoothness Constraint

• Add a new factor to distance-based smoothness into LAMM.

• This factor compare the distance between subswquent GPS readings and the distance between their associateed adjusted locations

• s

'candidate candidate

readings GPscurrent i

to equivalent matched-map thefrom distance the

and topoint from distancebetween difference D

pointsadjacent obetween twfit theof s'smoonthnes' reprents

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Verification

Location Area RoadSegments

GPSPoints

MMMatch

Boylston Boylston 539 91,287 88.3%

Chicago 11 km2 2752 15,591 91.7%

MM Match column represents the percent of GPS points that the map matchingalgorithm successfully matched to a road segment.

CDF for Boylstom

• Averaging approximately 5% improvement for each GPS points

• Averaging approximately 13% improvement for each GPS points

Conclusion

• Outdoor wireless positioning systems based on WiFi access points

• Understanding such a system and determining methods to improve accuracy could enhance the usefulness of WiFi positioning systems in providing location based services.