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Indoor Localization with a Crowdsourcing based Fingerprints Collecting

Indoor Localization with a Crowdsourcing based Fingerprints Collecting. System Architecture. Key Technology. Crowdsourcing based fingerprint extraction methods Localization Algorithms based on clustering theory. Fingerprints Extraction.

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Indoor Localization with a Crowdsourcing based Fingerprints Collecting

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  1. Indoor Localization with a Crowdsourcing based Fingerprints Collecting

  2. System Architecture

  3. Key Technology • Crowdsourcing based fingerprint extraction methods • Localization Algorithms based on clustering theory

  4. Fingerprints Extraction • In crowdsourcing model, multiple users will upload fingerprints via diverse devices • Our method extract fingerprint value based on RSS probability estimation, choose the optimum value from upload samples • Kernel density estimation eliminates device diversity than Gaussian probability estimation

  5. Fingerprints Extraction • Comparison of Gaussian and Kernel density estimation:

  6. Fingerprints Extraction • Based on kernel density estimation, choose optimum value from multiple upload RSS samples by multiple users by diverse devices.

  7. Localization Algorithm: MMC-KNN • MMC-KNN algorithm: find M most matched clusters, then apply KNN principle to choose out matched fingerprint • Use affinity propagation to process clustering:

  8. Localization Algorithm: MMC-KNN • How to find out the M most matched cluster? • Consider uploaded observation’s connections and similarities with all exemplars • Apply affinity propagation again and get responsibility vector: • choose the M most matched cluster by sort this responsibility vector

  9. Localization Algorithm: MMC-KNN • Assign a weight factor to each cluster’s fingerprints • Apply a grid window filter to filter a region which has the maximum weight, with the purpose to restrict KNN applied to a bursting region

  10. Real-time experimental testbed • Average error distance with different matched cluster number and grid window size for Nexus-S

  11. Real-time experimental testbed • 220 observation’s error distance statistic with best performance parameters for Nexus-S

  12. Real-time experimental testbed • CDF of location error distance for different algorithms

  13. Real-time experimental testbed • Comparison of different types devices’ location performance under diverse fingerprint databases

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