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Presented by: Xi Du, Qiang Fu

RADAR: An In-Building RF-Based User Location and Tracking system Paramvir Bahl and Venkata N. Padmanabhan Microsoft Research. Presented by: Xi Du, Qiang Fu. Overview. Related Work Methodology - The RADAR System - The RADAR test bed Algorithm and Experimental Analysis

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Presented by: Xi Du, Qiang Fu

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  1. RADAR: An In-Building RF-Based User Location and Tracking systemParamvirBahl and Venkata N. PadmanabhanMicrosoft Research Presented by: Xi Du, Qiang Fu

  2. Overview Related Work Methodology - The RADAR System - The RADAR test bed Algorithm and Experimental Analysis - Empirical Method - Radio Propagation Model Conclusion

  3. Related Work IR-Based Systems RF-Based in cellular systems Global Positioning System (GPS)

  4. The RADAR System Implemented purely in software Functional Components - Base Stations (Access Points) - Mobile Users Fundamental Idea in RADAR - Signal Strength is a function of the receiver’s location - Road Maps Techniques to build the Road Maps - Empirical Method - Radio Propagation Model Search Techniques - Nearest Neighbor in Signal Space (NNSS) - NNSS Avg. - Viterbi-like Algorithm

  5. The RADAR Testbed Base Station - Pentium-based PC - FreeBSD 3.0 Mobile Host - Pentium Based laptop - Windows 95

  6. Data Collection Key Step in this approach Mobile host broadcast 4 UDP packets per second with a 6-byte payload Each base station records the signal strength with timestamp (t, bs, ss) Record the orientation (t,x, y, d) Collected SS in 70 distinct locations with 4 directions, each with 20 samples

  7. Data Processing Traces are mergedinto a table consisting of tuples of the format [ x, y, d, ss(i), snr(i) ], i€ {1,2,3} Layout Information Specify the coordinates of each room and three base stations

  8. Algorithm and Experimental Analysis

  9. Empirical Method 280 combinations of user location and orientation (70 distinct points, 4 orientations on each point) Uses the above empirical data to construct the search space for the NNSS Algorithm Algorithm (Emulates the user location problem) 1. Picks one location and orientation randomly 2. Searches for a corresponding match in the rest of the 69 points and orientations Comparison with - Strongest Base Station - Random Selection

  10. Error Distance Values

  11. Empirical Method (Cntd.) N1 Multiple Nearest Neighbor Increases the accuracy of the Location Estimation T G N2 N3 Figure : Multiple Nearest Neighbors T – True Location G – Guess N1,N2,N3 - Neighbors

  12. Empirical Method (Cntd. ) Tracking a Mobile User - Analogous to the user location problem - New Signal Strength data set - Window size of 10 samples - 4 Signal Strength Samples every second Limitation of Empirical Method - To start off with needs an initial signal strength data set - Relocation requires re-initialization of the initial data set.

  13. Radio Propagation Model Introduction - Alternative method for extracting signal strength information based on mathematical model of indoor signal propagation -Reduce RADAR’s dependence on empirical data Issues - Reflection, scattering and diffraction of radio waves - Needs some model to compensate for attenuation due to obstructions Models - Rayleigh Fading Model : Assumes equal strength of all arriving signals - Rician Distribution Model : Complex model parameters - Floor Attenuation Factor

  14. Wall Attenuation Factor n: The rate at which the path loss increases with distance Signal power at reference distance d: Transmitter-receiver separation distance C: Maximum number of walls up to which the attenuation factor makes a difference nW: Number of walls between the transmitter and the receiver WAF: Wall Attenuation Factor

  15. WAF and C are estimated by measuring the signal strength of line-of-sight transmission and that of non-line-of-sight transmission with known number of walls. WAF = 3.1 dBm C = 4 Model simplification: linear relationship between the theoretically-predicted signal strength and the logarithm of the distance between the transmitter and receiver Wall Attenuation Factor

  16. Radio Propagation Model Advantages: - Cost Effective - Easily Relocated

  17. Future Work User Mobility Profile - Information about the likelihood of user’s current location based on old information - Aliasing effect gets reduced to a large extent Based Station based environmental profiling - Adds robustness to the system - Reduces inaccuracy

  18. Conclusion RF-based user location and tracking algorithm is based on - Empirically measured signal strength model -- Accurate - Radio Propagation Model -- Easily relocated. RADAR could locate users with high degree of accuracy. Median resolution is 2-3 meters, which is fairly good Used to build “Location Services” - printing to the nearest printer - navigating through a building

  19. Criticisms No mention of the response time of the System Not clear how well RADAR would work in a real-world setting Unsuitable for rapid development due to the initial infrastructure required Security & Privacy Issues associated with localization.

  20. Thanks!!

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