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Part of the Joint Project by HKBU, HKIVE and Several Local Mobile Service

Part of the Joint Project by HKBU, HKIVE and Several Local Mobile Service Providers for Accurate Low-cost Mobile localization. S upport V ector R egression for Location Estimation Using GSM Propagation Data. Dr. Chun-hung Li

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Part of the Joint Project by HKBU, HKIVE and Several Local Mobile Service

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  1. Part of the Joint Project by HKBU, HKIVE and Several Local Mobile Service Providers for Accurate Low-cost Mobile localization Support Vector Regression for Location Estimation Using GSM Propagation Data Dr. Chun-hung Li Department of Computer Science Hong Kong Baptist University June, 2003

  2. GSM Localization via Missing Value Insensitive Support Vector Regression Contents • Introduction • Related Works • SVR via Missing Value Insensitive Kernel • Simulation & Field Test • Q & A

  3. GSM Localization via Missing Value Insensitive Support Vector Regression Introduction • Task • To estimate the location of a mobile device using the information based on the GSMNetworks • Approach -- Network-based Solutions • Provide the location service using the network information without modifying the mobile phone • Baseline Accuracy • Federal Communications Commission rule- 100m (67% of the time)

  4. GSM Localization via Missing Value Insensitive Support Vector Regression Introduction – GSM Network Information • Returned from the mobile phone side • Serving Cell ID • BSIC • BCCH No • Received signal strength (dBm) • Other Station Information • Station Position (x & y) • Height • Bearing • Cell Type • Antenna Type • Station Power strength (dBm) • …… 1 3 2 4

  5. GSM Localization via Missing Value Insensitive Support Vector Regression Related Works - Network-based solution • Precise time and direction based methods • - TOA: Time of Arrival • - AOA: Angle of Arrival • - TDOA: Time-Difference of Arrival • - Require Synchronization Clock or Smart Antennas • SignalStrength Attenuation Modeling Approach • - Mapping signal strength into distance • --e.g. Free Space Model, HATA model, … • - Recover coordinate from distance • -- Cell-ID, Weighted CG • -- Tri-lateration

  6. GSM Localization via Missing Value Insensitive Support Vector Regression Related Works – Weighted CG & Cell-ID • Based on Free Space Model • The distance and the received signal strength is an inversely proportional function • Or Approximation: • Weighted Central of Gravity (CG) • Smaller Distances -> nearer to stations • If N is 1, obtain the Cell-ID Method where N is the number of neighboring base stations, Δs is the signal strength falloff in dBm

  7. Transmitter r3 r2 r1 Estimated mobile location GSM Localization via Missing Value Insensitive Support Vector Regression Related Works – Circular Trilateration

  8. GSM Localization via Missing Value Insensitive Support Vector Regression Related Works – Machine Learning Approach • More robust calibration of Propagation Models • Statistical Modeling Approach • Directly map signal strength to location output • Wireless LAN Positioning via • Neural Network, • Support Vector Classification/Regression • Fingerprinting Method

  9. GSM Localization via Missing Value Insensitive Support Vector Regression Why using Machine Learning Approaches • Hard to Obtain a Parametric Model • Terrain Factors, multi-path, occlusion, … • Noise Measurement, Weather Condition, … • Comparably Easy to get a lot of data • Fit a nonparametric model to the data • No need for domain experts/domain models • Changes in models/parameters can be re-learned

  10. GSM Localization via Missing Value Insensitive Support Vector Regression Introduction to Support Vector Regression • Adopting a mapping to transform all signal strength readings at a location into a series of descriptors: • E.g. • Linearly regress the series of descriptors into the position output W is of the same length as the long descriptor vector

  11. GSM Localization via Missing Value Insensitive Support Vector Regression Introduction to Support Vector Regression – Cont. • w by solution is the linear combination of a set of descriptor vectors from l training data • E.g. • Location output (x or y) : • The key is to seek a Kernel function Where r(i) denotes the i-th signal vector used for training

  12. GSM Localization via Missing Value Insensitive Support Vector Regression Incompetent Conventional Kernels • e.g. RBF Kernel: • S is a severely sparse vector • Only 3~9 signals are retrievable • e.g. two sample signal reading Vectors: • Impute empty cells by values: • Too many! & What’s the physical meaning?

  13. GSM Localization via Missing Value Insensitive Support Vector Regression A New Missing Value Insensitive Kernel • Sum of Exponential Kernel (SoE) • Where • It is a valid kernel by proof • Recently proved to be a variant of the 1st-order RBF-ANOVA Kernel

  14. GSM Localization via Missing Value Insensitive Support Vector Regression A Kernel Matrix Evaluated from SoE

  15. GSM Localization via Missing Value Insensitive Support Vector Regression Experimental Results – Simulation Study • Model adapted from [Roos 2001] • Adding Occlusion and Noise effects • Experiment Settings • 30 km2 Data Collection Region • 640 Training Markers, 200 Testing Markers • 64 Base Stations, 8 receivable

  16. GSM Localization via Missing Value Insensitive Support Vector Regression Experimental Results – Field Data Test Data Collection

  17. GSM Localization via Missing Value Insensitive Support Vector Regression Experimental Results – Field Data Test • Experiment Settings • A 350 x 550m data Collection Region • Total 15 Markers • 120 set of readings / marker • 50 Base Stations, 7~9 receivable

  18. GSM Localization via Missing Value Insensitive Support Vector Regression Experimental Results – Field Data Test • Experiment Results • For SVR Training: • 9 Markers for Training • Multiple sets of readings from each training marker • For SVR Testing: • Predict one location for a single set of readings • Predict one location for multiple sets of readingsacquired at the same site and in a short interval

  19. GSM Localization via Missing Value Insensitive Support Vector Regression 1) 8 of 120 sets of training readings from each of the 9 of 15 markers2) 120 sets of testing readings from the remain 6 of 15 markers3) mean error = 47m

  20. GSM Localization via Missing Value Insensitive Support Vector Regression 1) predict 120 sets of readings in each testing marker to one location2) interval: 2 min3) mean error = 21m

  21. GSM Localization via Missing Value Insensitive Support Vector Regression or shown in following diagram:

  22. GSM Localization via Missing Value Insensitive Support Vector Regression Q & A

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