1. ArrayTrack : A Fine-Grained Indoor Location System Jie Xiong, Kyle Jamieson USENIX NSDI ‘13
Jungmin Yoo
*some slides are from author’s slides

2. Contents
Introduction
ArrayTrack
Evaluation
Conclusion

3. INTRODUCTION

4. Location system Outdoor : Global Positioning System (GPS)
Provides accurate location services
Signals fade in indoor environments
Precise indoor location system
Augmented reality-based building navigation, social networking etc.
Known technologies
Not accurate enough (WiFi)
Require dedicated infrastructure (ultrasound)

5. Two observations about WiFi
Increasing number of antennas on an access point (AP)
Improve the capacity and coverage
WiFi is ubiquitous and densely deployed
On airplanes, subways and buses

6. OVERVIEW

7. Overview APs overhear a client’s transmission
AP leverages multiple antennas to generate physical angles of arrival (AoA) of a client’s signals
AoA spectrum : power versus bearing
at one AP (*bearing : 방향)
With multiple APs, central server synthesizes AoA spectra to obtain a location estimate for the client

8. The challenge: multipath reflections
Problem #1: Strong multipath reflections indoors
Problem #2: Direct path attenuated or completely blocked
Direct path signal may not be the strongest

9. Multipath suppression algorithm
Key observation
Direct path bearing is more stable than reflection path bearings when client moves slightly

10. Multipath suppression algorithm
Given: AoA spectra from two nearby locations
Find the peak bearings in each AoA spectrum
Discard any peak not paired with a peak in the other AoA spectrum

11. ARRAYTRACK

12. 1. Detection and recording
Content of packet and modulation type do not matter
Works with any part of a packet
ArrayTrack utilized the most robust preamble part

13. 1. Detection and recording
Very small part (sample) of a packet needed
In the absence of noise, one sample works
Employ multiple samples for averaging to remove noise

14. 1. Detection and recording
Diversity synthesis :
Record 10 samples from the first preamble half and another 10 samples from the second preamble half with different antennas

15. 2. AoA spectrum generation
How an AP can compute AoA?
Analyzing received phase at the AP

16. 2. AoA spectrum generation

17. 2. AoA spectrum generation
MUSIC algorithm [Schmidt, 1986] for AoA spectrum estimation
MUltiple SIgnal Classification
Analyzes received phase from source to each antenna
Does not work well for phase-synchronized signals
Spatial Smoothing (SS) [Shan et al, 1985]
Averages incoming signals across groups of antennas
Reduces correlation

18. 3. AoA spectra synthesis N APs generate N AoA spectra
For a random position X, the likelihood of being at X is a multiplication of probabilities from multiple Aps
Evaluate at each point in a 10cm by 10cm grid

19. EVALUATION

20. Implementation
AP : two WARPs, each with four radio boards (eight antennas)
4-16 antennas placed in a linear arrangement, spaced at λ/2
Clients : Soekris boxes equipped with radios
Backend location server : implemented in Matlab (1,000+ LoC)

21. Testbed environment
Backend server has knowledge of each AP’s location

22. Effects of number of APs
No multipath suppression
Average error decreases with an increasing number of APs
With 6 APs, we can achieve 26cm accuracy

23. Effects of number of APs
Heatmap example of increasing number of APs

24. Effects of multipath suppression
With 6 APs, we can achieve 23cm accuracy
The fewer APs, the more important is multipath suppression

25. Number of antennas at AP
With more antennas at each AP, we can achieve a more accuracy

26. Client-AP differences in height

27. Conclusion ArrayTrack A robust, precise indoor location system
Median accuracy of 23cm (6APs) and 1m (3APs)
Novel multipath algorithm and diversity synthesis algorithms
Uses only the WiFi infrastructure nearby
No cost