1. Zsolt T. Kardkovács Budapest University of Technology and Economics Dept. Telecommunications and Mediainformatics MŰEGYETEM 1782 High Precision Indoor Localisation by Non-Linear Noise Reduction

2. Problem statement Is it an important task? No, people usually know where to go RFID solutions are better suite for this problem Yes, location based services are needed in-door i-home solutions require in-door positioning if one of the following applies in-door traffic logging is important in-door tracking is important interactivity (or communication) is a requirement fast & easy set-up is necessary you need cheap & general solution in a single device 6/6/2014FuturICT 2009, High-precision in-door localization2

3. Problem statement This is a solved problem, isnt it? Yes, there are great many in-door positioning applications, e.g. map based solutions if background knowledge is available great many training data is available needs access point (AP) programming No, they are either need lot of background work or they have a low precision rate (RME > 2m) 6/6/2014FuturICT 2009, High-precision in-door localization3

4. Focus on the resources Ideal in-door positioning Needs No map information No information on AP locations No AP programming Low amount of training data Low computational resources It can be placed either on client or AP side Fast Adaptive to small changes Easy re-calibration if necessary 6/6/2014FuturICT 2009, High-precision in-door localization4

5. Foundations Received Signal Strength Indication (RSSI) Close (visible) space Relaxation: quadratic to distance Simple to calculate Precise, although there is a sphere with iso-RSSI points …if possible priories close space information Far (non-visible) space Signals suffer from diffraction, reflection, and multipath propagation effect Vague, there are a limited number of iso-RSSI point …approximation is required …the only fix point is the shortest route from AP to target 6/6/2014FuturICT 2009, High-precision in-door localization5

6. RSSI A kind of distance function log-linear to RF path length: where d[m] and d[dBm] – distances in meters and dBm respectively Φ is a frequency dependent parameter (for Ch. 7 it is 0.009) β is a relaxation coefficient (in ideal environment it is 20) α is a signal to noise ratio coefficient (AP dependent) Channel is noisy - it varies in distance in time in motion 6/6/2014FuturICT 2009, High-precision in-door localization6

7. Our solution Observation only the first RSSI is important others can be used for noise reduction(!) Visible space is preferred within 1.5 d[m] range use AP coordinates farther use other APs to determine the exact location Far space A range of RSSI is valid for any location in d[m] space if for any training data point calculated distance is 0 if and d[m] otherwise K-nearest neighbor algorithm 6/6/2014FuturICT 2009, High-precision in-door localization7

8. Evaluation If at least 2 APs are available for each location RMSE is 3,8m (RME < 2m) 63% precision, i.e. within 1.5m (31% within 0.5m) The target location is within the top 3 places (85%) IEEE ICDM challenge 5 th prize award (the best European competitor) Other features Performance not depends on the size of training data Needs no additional configuration time Needs no information on APs location or Ch. Information Needs no map information Easy deployment Extensions GSM based out-door positioning (<15m in 83%, city) If tracking information is available precision is higher (RMSE 2,8m) 6/6/2014FuturICT 2009, High-precision in-door localization8

9. Zsolt T. Kardkovács Budapest University of Technology and Economics Dept. Telecommunications and Mediainformatics MŰEGYETEM 1782 High Precision Indoor Localisation by Non-Linear Noise Reduction Thank you for your attention and time