Toward Better Indoor Localization: Cooperative Localization and Estimation Fusion

1. Toward Better Indoor Localization:Cooperative Localization and Estimation Fusion Gary Chan, Associate Professor The Hong Kong University of Science and Technology

2. Outline • Indoor localization techniques • Improving accuracy on current localization infrastructure • Cooperative (Peer-to-peer) localization • Collaborative mobile devices • Simulation results • Estimation fusion • Optimally combining multiple estimations • Preliminary results • Conclusion

3. Indoor Localization • Mobile device capabilities and penetration of wireless access networks  Many new types of mobile services become viable • Location based service (LBS) is with great commercial potential • Indoor LBS • Find the closest restaurant, the best-buy-of-the-day of a shop, etc. • Better localization • Better service • Better routing for correctness and bandwidth efficiency • LBS relies on accurate localization of client devices in order to provide high quality services

4. Challenges of Indoor Localization • Global Positioning System (GPS) only works well outdoor • Indoor environment • Complicated layout leads to complex fading, shadowing and interference, affecting its accuracy • Line-of-sight (LoS) not easily achievable indoor • Requirements • High accuracy • Computationally light-weighted • Privacy • Etc.

5. Localization Techniques • Measurements • Distance-based (time of arrival, time difference of arrival, received signal strength, etc.) • Angle-based (Angle of arrival) • Pattern-based • Motion, velocity and direction • Electromagnetic • Etc. • Techniques • Trilateration (for distances) • Triangulation (for angles) • Inertial navigation systems (INS) • Fingerprinting • Optimization • etc.

6. L3 trilateration r3 embedding methods d3 d2 r2 r1 N d1 L1 L2 Distance-based techniques • Measure distances among nodes and infrastructure nodes/landmarks • Use mathematical property to estimate lcoation, e.g. • Trilateration • Graph embedding methods

7. Pros and Cons • Pros • Simple • No expensive hardware • Often requires clock synchronization to calculate distances • Accuracy is prone to signal fluctuation and clock synchronization

8. 2-angle triangulation 3-angle triangulation Angle-based Techniques • Measure angles between the node and landmarks • Use mathematical properties to estimate location, e.g. • 2-angle triangulation (angles measured at landmarks) • 3-angle triangulation (angles measured at mobile node) trilateration

9. Pros and Cons • Pros • Less sensitive to signal attenuation • Simple calculation (transformable into trilateration) • Cons • Requires special hardware (directional antennas) to measure angles • Can be affected by reflections or multipaths

10. BS1 BS3 BS2 Pattern-based Techniques • Associate observed patterns with location • Training Phase • Measure signal patterns at reference points • Establish a mapping between them • Online Phase • Observe pattern at unknown position • Compare with trained data • Estimate location

11. Pros and Cons • Pros • Fast estimation (just a look up) • Accurate (if the map is current) • Cons • Time-consuming and labor-intensive training phase • Map has to be current; not adaptive to environmental changes

12. Electro-Magnetic Tag Approach • Technologies • Infrared (IR) tags • Ultrasonic • Radio Frequency Identification (RFID) • UWB (Ultra-wide band) • Etc. • Characteristics • Higher accuracy due to shorter range • Some require line-of-sight • This category of techniques may be part of a localization system and provides alternative references to improve accuracy

13. Comparison of Tag Schemes

14. Inertial Navigation System (INS) • Key components • Motion sensor • Rotation sensor • Acceleration sensor • Etc. • Characteristics • Continuously compute location based on previous location and sensor information • No external references needed • Accumulation of errors over time • Performance of INS largely depends on drift compensation scheme in order to reduce propagation error • Integral computation • Computationally intensive and error-prone

15. Factors of Inaccuracies • Not all techniques are 100% accurate • Signal fading or transient signal fluctuation • Measurement noise or uncertainty • Clock synchronization or inaccuracy • Landmark density • Accumulation of errors over time (for INS) • Environmental changes (for pattern-based/fingerprinting) • Lack of updates or measurement granularity • Etc.

16. How to Achieve Higher Accuracy? • Augment upon the existing infrastructure • Providing a natural transition path toward higher accuracy • Cost-effective • No expensive hardware • For populated areas • Cooperative (Peer-to-peer) estimation • Mobiles help each other to achieve better accuracy • Multiple estimations • Estimation techniques do not have to be treated in isolation • Combining their estimations for better accuracy

17. Collaborative Localization Using Peer-to-Peer Technique

18. Infrastructure and Mobile Noes • Infrastructure • Some landmarks or access points (APs) to provide basic localization • Due to deployment cost, the accuracy is not high • Mobile nodes • Limited computational power, transmission range and battery life • High density over the infrastructure network • Form a mobile ad-hoc network to better estimate their locations • Achieving better localization using cooperative mobile nodes

19. 3 5 2 4 The Localization Scheme: Local Estimation (1) • Construct a table of neighbors by varying a node’s transmission range • Quantized Distance Vector (QDV) construction QDV 2 1 5 2 1 4 3 Identifier Distance Level

20. 5 6 3 1 4 2 MDS Location Estimation (2) • mISOMAP • Collect QDVs from neighbors • Compile QDVs locally • Generate embedding using Multi-dimensional Scaling (MDS)

21. 5 6 3 1 5 5 4 6 6 3 3 1 1 4 2 4 2 2 5 6 3 1 4 2 Infrastructure to Fix the Embedding • Embedding transformation • Requires at least 3 references to “fix” the embedding Reflection Translation Rotation

22. Localization Spreads Like a Ripple from Landmarks • Starts with a landmark doing the local estimation, then spreads to its neighboring nodes • Nodes receiving location updates become references of others

23. Combining Estimations from Different Landmarks Together • Map refinement • Combines several relative positions to generate an absolute position by minimizing: px : absolute position pLi : relative position to BN i dxLi : distance from BN i

24. Locations are Well Estimated Real positions Estimated positions Normalized Average actual distance error = 0.2805Normalized Average relative distance error = 0.24883

25. Summary • A collaborative localization scheme • Distance-based • Improves the accuracy of infrastructure network • Only requires quantized distance measurement • Robust to measurement noise • Only requires signal power control • No special hardware requirement • No global synchronization • Only involves neighbor communication • Low power consumption • Fully distributed • Supports network dynamics

26. Estimation Fusion

27. Research Motivation • Many indoor location techniques deployed • Wi-Fi, RFID, GPS, INS, etc. • Locations are estimated in isolation • Different level of errors • Due to measurement noise, base-station density, calibration accuracy, etc. • A handheld may have all these estimations at the same time

28. Objective: Combine, or fuse, estimations to attain better localization accuracy • Characterization of estimation errors of different localization techniques • Angle of arrival (AOA) • Time different of arrival (TDOA) • Roundtrip time of flight (RTOF) • Inertial Navigation System (INS) • Given errors, optimally combine them • With efficient, simpleand distributed algorithm • With environmental or topological constraints

29. Localization Error: Angle of Arrival (AOA) • AOA: angle between BS and MS • : AOA • : coordinates of base station i • : coordinates of the mobile • : measurement noise

30. Estimation Error • Variance of the estimation • Related to 2 factors • Distance between mobile and BS • Variance of measurement noise

31. Close Match Between Simulation and Analysis • Number of BS = 6

32. Estimation Error Decreases with Base Stations • = 10 degrees

33. Estimation Error: Time Difference of Arrival (TDOA) • TDOA • Get time difference from the mobile to different base stations • Draw hyperbola for every set of time difference • Obtain the intersection point as the mobile location • Time Difference: • : • : • : Distance measurement noise • : Synchronization noise

34. Estimation Error Decreases with Base Stations

35. Roundtrip Time of Flight (RTOF) • TDOA requires synchronization of clocks between base stations and devices • TROF does not require that • System components: • Clock • Base stations • Noise assumed: • Clock shift:

36. Round Trip Time of Flight RTT: Round Trip Time

37. Estimation Error

38. Error Analysis of Inertial Navigation System • System components: • Gyroscope : measure orientation • Accelerometer: measure acceleration • Two noise assumed: • Gyroscope: • Accelerometer:

39. Error Analysis Possible Estimation Distance Error Possible Estimation Degree Error

40. Estimation Error w.r.t. the Accelerometer Error

41. Super-linear Increase in Estimation Error with the Duration of Using INS

42. Estimation Error is Similar to a Normal

43. Estimation Fusion • Given a number of estimations with location uncertainties, how to optimally combine them? • Estimation i: Xi ~ N( xi , sigmai), Yi ~ N( yi , sigmai) • Find a coordinate minimizing the expected distances to all these estimations

44. Problem Formulation • Objective function

45. Optimal Solution • Optimal solution is a point estimate: • Closed-form expressions • Simple and efficient computation

46. Estimator Error Decreases with the Number of Estimators

47. Topological Constraint: Alley

48. Topological Constraint: Wall

49. Topological Constraint: Corner

50. Topological Constraint: Room