Localization

1. Localization Updated on 11/6/2018

2. Location • Source of wireless signals • Wireless emitter • Location of a mobile device • Some devices, e.g., cell phones, are a proxy of a person’s location • Used to help derive the context and activity information • Location based services • Privacy problems

3. Location • Well studied topic (3,000+ PhD theses??) • Application dependent • Research areas • Technology • Algorithms and data analysis • Visualization • Evaluation

4. Representing Location Information • Absolute • Geographic coordinates (Lat: 33.98333, Long: -86.22444) • Relative • 1 block north of the main building • Symbolic • High-level description • Home, bedroom, work

5. Some outdoor applications E-911 Bus view Car Navigation Child tracking

6. Some indoor applications Elder care

7. No one size fits all! • Accurate • Low-cost • Easy-to-deploy • Ubiquitous • Application needs determine technology

8. WiFi Beacons Ad hoc signal strength GPS Physical contact VHF Omni Ranging Ultrasonic time of flight Laser range-finding Array microphone Infrared proximity Stereo camera E-911 Lots of technologies! Ultrasound Floor pressure

9. Wireless Technologies for Localization

10. Localization Techniques • Range-based algorithms • Range-free algorithms • Fingerprinting

11. Range Based Algorithms • Rely on the distance (angle) measurement between nodes to estimate the target location • Approaches • Proximity • Lateration • Hyperbolic Lateration • Angulation • Distance estimates • Time of Flight • Signal Strength Attenuation

12. Approach: Proximity • Simplest positioning technique • Closeness to a reference point • Based on loudness, physical contact, etc

13. Approach: Lateration • Measure distance between device and reference points • 3 reference points needed for 2D and 4 for 3D

14. Approach: Hyperbolic Lateration • Time difference of arrival (TDOA) • Signal restricted to a hyperbola

15. Approach: Angulation • Angle of the signals • Directional antennas are usually needed

16. Distance Estimation • Multiple the radio signal velocity and the travel time • Time of arrival (TOA) • Time difference of arrival (TDOA) • Compute the attenuation of the emitted signal strength • RSSI • Problem: Multipath fading

17. Distance Estimation: TOA • Distance • Based on one signal’s travelling time from target to measuring unit • d = vradio * tradio • Requirement • Transmitters and receivers should be precisely synchronized • Timestamp must be labeled in the transmitting signal

18. Distance Estimation: TDOA • Distance • Based on time signals’ travelling time from target to measuring unit • d = vradio * vsound * (tradio- tsound) / (vradio – vsound)) • Requirement • Transmitters and receivers should be precisely synchronized • Timestamp must be labeled in the transmitting signal • Line-Of-Sight (LOS) channel

19. Distance Estimation: RSSI • Distance • Based on radio propagation model • Requirement • Path loss exponent η for a given environment is known

20. Range Free Algorithms • Rely on target object’s proximity to anchor beacons with known positions • Neighborhood: single/multiple closest BS • Hop-count: anchor broadcast beacons containing its location and hop-count • Area estimation:

21. Fingerprinting • Mapping solution • Address problems with multipath • Better than modeling complex RF propagation pattern

22. Fingerprinting: Steps • Step1 • Use war-driving to build up location fingerprints (i.e. location coordinates + respective RSSI from nearby base stations) • Step2 • Match online measurements with the closest a priori location fingerprints

23. Fingerprinting: Example

24. Fingerprinting: Features • Easier than modeling • Requires a dense site survey • Usually better for symbolic localization • Spatial differentiability • Temporal stability

25. Summary of Localization Techniques

26. Localization Systems • Distinguished by their underlying signaling system • IR, RF, Ultrasonic, Vision, Audio, etc [13]

27. GPS • Use 24 satellites • TDOA • Hyperbolic lateration • Civilian GPS • L1 (1575 MHZ) • 10 meter acc.

28. Active Bat • Ultrasonic • Time of flight of ultrasonic pings • 3cm resolution

29. Cricket • Similar to Active Bat • Decentralized compared to Active Bat

30. Cricket vs Active Bat • Privacy preserving • Scaling • Client costs Active Bat Cricket

31. RADAR • WiFi-based localization • Reduce need for new infrastructure • Fingerprinting 31

32. Place Lab • “Beacons in the wild” • WiFi, Bluetooth, GSM, etc • Community authored databases • API for a variety of platforms • RightSPOT (MSR) – FM towers

33. Computer Vision • Leverage existing infrastructure • Requires significant communication and computational resources • CCTV

34. Performance Metrics • Accuracy • Mean distance error (RMSE) • Precision • Variation in accuracy over many trials (CDF of RMSE) • Robustness • Performance when signals are incomplete • Cost • Hardware, energy

35. Performance Evaluation

36. Performance Evaluation

37. E-V Loc: Goal • Find a specific person’s accurate location based on his electronic identifier and visual image - Publication: Boying Zhang, Jin Teng, Junda Zhu, Xinfeng Li, Dong Xuan, and Yuan F. Zheng, EV-Loc: Integrating Electronic and Visual Signals for Accurate Localization, in ACM MobiHoc’12.

38. E-V Loc: Problem Formulation • Input: a target object’s electronic identifier EID*, a set (in a short time span) of E Frames with clear EIDs and the corresponding V Frames with possibly vague VIDs • Output: the target object’s accurate position together with its visual appearance VID*

39. E-V Loc: Work Flow Need more signal samples?

40. E-V Loc: Nature of Our Solution • E-V matching • Uses electronic and visual signals as target object’s location descriptors in E frames and V frames • Matches the corresponding E and V location descriptors using Hungarian algorithm

41. E-V Loc:Localizing with Distinct VIDs • Best match problem between EIDs and VDs EIDs VIDs

42. E-V Loc: Incremental Hungarian algorithm • Find the best match between the EIDs and VIDs in each pair of E and V frame • Iteratively perform the matching until a threshold is satisfied • The threshold is derived based on the variance model of EIDs and VIDs

43. E-V Loc:Localizing with Indistinct VIDs • Multi-dimensional best match problem • Between EIDs and VIDs • Among VIDs

44. E-V Loc: Two-dimensional Hungarian Algorithm • Finding correspondence between different VIDs in neighboring frames • Based on the correspondence, generating a consistent set of VIDs in all frames • Using incremental Hungarian algorithm to perform the match

45. Flash-Loc: Flashing Mobile Phones for Accurate Indoor Localization - Publication Fan Yang, Qiang Zhai, Guoxing Chen, Adam C. Champion, Junda Zhu and Dong Xuan, Flash-Loc: Flashing Mobile Phones for Accurate Indoor Localization, in Proc. of IEEE International Conference on Computer Communications (INFOCOM), April 2016.

46. Outline Overview Flash-Loc Design Localization Integration Implementation and Evaluation Summary

47. Overview Accurate, fastand reliablelocalization of a flash light source User ID is carried by light flashes to distinguish different users It can work with visibleand invisiblelight It can be implemented with commercial off-the-shelf devices.

48. Working Scenario

49. Flash-Loc Methodology (1) • Where: Fast, accurate and reliable positioning of flash light source with calibrated cameras • Flashes with abrupt brightness changes are clear visual indicators of users • Light flashes travel long distances before diminishing in intensity, this system can work in a wide range of area

50. Flash-Loc Methodology (2) • Who: Distinguishable ID is delivered via flash light pattern • Flash light with controllable flash pattern • Flash ID is physically carried on flash pattern Who + Where = Object Localization