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Localization of Mobile Users Using Trajectory Matching

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

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Localization of Mobile Users Using Trajectory Matching

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  1. Localization of Mobile Users Using Trajectory Matching ACM MELT’08 HyungJune Lee, Martin Wicke, BranislavKusy, and LeonidasGuibas Stanford University

  2. 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

  3. 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 measurements RSSI graph

  4. Outline • Trace Space • Localization algorithm • Training Phase with RBF construction • Localization Phase • Evaluation • Conclusion and Future work

  5. Trace Space • Traces ofRSSI readings form a trace space . • Each trace T corresponds to a location • Learn to match a trace to a positioni.e., L(∙): → R2 3 (x1, y1) (x1, y1) 2 1 x y → R2 T = L = : (x2, y2) 4 5

  6. Training Phase with RBF Fitting • Training input r in trace space • Training output p inR2 space • Solve linear systems of training data by least-squares • Obtain L(∙) function 6

  7. Localization Phase • 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 Location X LX (T) LY (T) Location Y 7 Illustration from “Scattered Data Interpolation with Multilevel B-Splines” [Lee97]

  8. Evaluation 8 • 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 9 7 1 6 10 1 5 4 2 3 RSSI graph

  9. Evaluation • Training phase: (a), (b), (c), (d), (e) • Testing phase: (f), (g), (h), (i) • 5 runs for each path • Error measures • Position error • Path error

  10. History size k Influence of Historical data 2.4 m 1.28 m

  11. Other Link Quality Measures 2.02 m 1.74 m 1.28 m

  12. 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

  13. Future work • Prediction of future location • Scalability • Dynamic time warping for different speed

  14. Questions? HyungJune Lee abbado@stanford.edu

  15. Radial Basis Function Fitting(Backup) • Multi-quadratic function • By least-squares

  16. # of RBF centers Nc Influence of # of RBF centers Nc(Backup)

  17. Burst window size b Influence of Average Window Size b (Backup)

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