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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
0,05.05
-1max
)max(
icandidate
candidatebest
d
i
lookaheadij
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
D
5 where
1
1
MMj
MMj
jj
i
lookaheadijs
pp
pp
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.