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Localization of Mobile Users Using Trajectory Matching. ACM MELT’08 HyungJune Lee, Martin Wicke , Branislav Kusy , and Leonidas Guibas Stanford University. Motivation. Location is an important and useful resource Push local information to nearby mobile users - PowerPoint PPT Presentation
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Localization of Mobile Users Using Trajectory Matching
ACM MELT’08HyungJune Lee, Martin Wicke,
Branislav Kusy, and Leonidas GuibasStanford University
Motivation
• Location is an important and useful resource– Push local information to nearby mobile users
• Restaurant, Café, Shopping center on sale, …
– Building automation, etc.
• GPS not available– Indoor, mobile environment
• ~1m-accuracy– Usable for location-based service
2
Motivation• RSSI-based localization• Indoor setting
– Due to reflection, refraction, and multi-path fading,specific model does not work
– More severe link variation caused by mobility
• Range-free methods– Connectivity & Triangulation:
DVhop[Niculescu03] , APIT[He05]– RSSI pattern matching:
RADAR[Bhal00], MoteTrack[Lorincz07]– Bayesian inference & Hidden Markov Model:
[Haeberlen04], [Ladd04], LOCADIO[Krumm04]
• Idea: Use historical RSSI measurements3
RSSI graph
Outline
• Trace Space• Localization algorithm
– Training Phase with RBF construction– Localization Phase
• Evaluation• Conclusion and Future work
4
Trace Space
• Traces of RSSI readings form a trace space .
• Each trace T corresponds to a location
• Learn to match a trace to a positioni.e., L( ): → ∙ R2
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Training Phase with RBF Fitting• Training input r
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• Training output p
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• Solve linear systems of training data by least-squares
• Obtain L( ) function∙
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Tr vectorsRSSI Nk ,
• Localization phase– Calculate the L ( ) given current trace ∙ T in test sets
• Sparse interpolation in trace space– Handles noisy input data gracefully– Extrapolates to uncharted regions
Localization Phase
7Illustration from “Scattered Data Interpolation with Multilevel B-Splines” [Lee97]
Location X
Location Y
LX (T)
LY (T)
RSSI graph
Evaluation• MicaZ motes
– CC2420 radio chip
• 10 stationary nodes• 1 mobile node• 14 waypoints location
• Ground-truth: (r(t), p(t))– Training RSSI vector r(t)– Training position p(t)
• linear interpolation between waypoints
8
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Evaluation• Training phase
: (a), (b), (c), (d), (e)• Testing phase
: (f), (g), (h), (i)• 5 runs for each path
• Error measures– Position error
– Path error9
Influence of Historical data
10
History size k
1.28 m
2.4 m
11
Other Link Quality Measures
1.28 m
1.74 m
2.02 m
Conclusion
• Historical RSSI values significantly increase the fidelity of localization (mean position error < 1.3 m)
• Our algorithm also works well with any link quality measurements, e.g., LQI or PRR, which allows flexibility of the algorithm
12
Future work
• Prediction of future location• Scalability• Dynamic time warping for different speed
13
Radial Basis Function Fitting(Backup)
• Multi-quadratic function
• By least-squares
15
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# of RBF centers Nc
Influence of Average Window Size b (Backup)
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Burst window size b