1. CAT: High-PreCision Acoustic Motion Tracking
Wenguang Mao, Jian He, Lili Qiu
UT Austin
MobiCom 2016

2. Why motion tracking?
Motion-based Games
Virtual Reality

3. Support motion-based interaction
Why motion tracking?
Smart Appliance
Support motion-based interaction

4. Possible solutions Vision based approach Needs extra hardware
Depends on lighting condition
Computationally heavy

5. Possible solutions RF based approach
WiFi : limited accuracy (e.g., 10 cm [Chronos16])
RFID: limited accuracy (e.g., 4 cm [RF-Idraw])
60 GHz waves: extra hardware not widely available
60GHz Antenna

6. Acoustic Signal Slow propagation – helpful to achieve high accuracy
Easily available speakers and mics – widely available
Low sampling rate – feasible for SW processing

7. CAT
𝒗𝒆𝒍
𝒅𝒊𝒔𝒕
CAT

8. Optimization Framework
Key Components
CAT
Distributed FMCW
Distance
Optimization Framework
Doppler Shift
Audio Samples
Velocity
Movement Trajectory

9. FMCW 𝑓 𝑡 𝑑 FMCW for propagation delay estimation
Less bandwidth usage than using a sharp pulse
Send a chirp whose freq. changes linearly over time
Estimate the frequency difference 𝑓
𝑡 𝑑 =𝑘𝑓, and 𝑡 𝑑 ~ distance travelled by the chirp 𝑓
𝑡 𝑑

10. Distributed FMCW Speaker (sender) and microphone (receiver):
Not known when the chirp is sent
Two-step distance estimation
Sampling rate offset
Drift compensation
Time
Frequency
Transmitted
Received

11. Two-Step Distance Estimation
Decompose distance 𝑅 𝑛 into two parts
Pseudo-transmission time
𝑅 𝑛 = 𝑅 𝑛 − 𝑅 1 + 𝑅 1
Reference point

12. Two-Step Distance Estimation
Decompose distance 𝑅 𝑛 into two parts
Pseudo-transmission time
𝑅 𝑛 = 𝑅 𝑛 − 𝑅 1 + 𝑅 1
Reference point

13. Pseudo-Transmission Time
𝑅 1
𝑅 𝑛
𝑅 𝑛 − 𝑅 1 ~ 𝑓 𝑛 − 𝑓 1
Time
Frequency
Transmitted
Received
𝑓 1
𝑓 𝑛
Pseudo-Transmitted

14. Two-Step Distance Estimation
Decompose distance 𝑅 𝑛 into two parts
Pseudo-transmission time
𝑅 𝑛 = 𝑅 𝑛 − 𝑅 1 + 𝑅 1
Reference point

15. Reference Point 𝐷 1 2 − 𝐷 2 2 = 𝐴 2 𝐷 1 − 𝐷 2 ~ ( 𝑓 1 − 𝑓 2 )
Doppler +
Doppler -
𝐷 1 2 − 𝐷 2 2 = 𝐴 2
𝐷 1 − 𝐷 2 ~ ( 𝑓 1 − 𝑓 2 )

16. Drift Compensation
Estimated distance drift over time

17. 1764 samples at the receiver
Drift Compensation
Due to imperfect clocks, the sender and the receiver have different the sampling rates
E.g., Hz (sender), Hz (receiver)
1764 samples at the receiver
1764 samples at the sender
Prop. delay
Chirp 1
Prop. Delay
Chirp 2
Chirp diff.

18. Drift Compensation

19. Drift Compensation

20. Doppler Shift Measurement
Measure frequency shift 𝑭 𝑺 between transmitted and received signals
Velocity is given by
𝐅 𝐒
𝒗=𝒄 𝑭 𝒔

21. Optimization framework
No error accumulation
Smooth the estimated results
Fusing distance and velocity measurements
Find position 𝒛 that fits the measurements best
Efficient algorithm for solving it
Incorporate IMU measurements
𝒊 𝒋 𝜶 𝒛 𝒊 − 𝒄 𝒋 − 𝒛 𝟎 − 𝒄 𝒋 − 𝒅 𝑭𝑴𝑪𝑾 𝒊,𝒋 𝟐 +
𝒊 𝒋 𝜷( 𝒛 𝒊+𝟏 − 𝒄 𝒋 − 𝒛 𝒊 − 𝒄 𝒋 − 𝒗 𝒅𝒐𝒑𝒑𝒍𝒆𝒓 𝒊,𝒋 ⋅𝑻)
Dist. measurement fitting error
Vel. measurement fitting error
Multiple tracking periods

22. Experiments 2D tracking with 2 speakers 2D tracking with 3 speakers

23. 2D Tracking Accuracy
2 cm
8 cm
6mm
(𝜶,𝜷)
𝒊 𝒋 𝜶 𝒛 𝒊 − 𝒄 𝒋 − 𝒛 𝟎 − 𝒄 𝒋 − 𝒅 𝑭𝑴𝑪𝑾 𝒊,𝒋 𝟐 +
𝒊 𝒋 𝜷( 𝒛 𝒊+𝟏 − 𝒄 𝒋 − 𝒛 𝒊 − 𝒄 𝒋 − 𝒗 𝒅𝒐𝒑𝒑𝒍𝒆𝒓 𝒊,𝒋 ⋅𝑻)
CAT is accurate and fusing distance/velocity significantly improves the performance

24. 3D Tracking
8-9 mm 3D tracking error

25. 4mm trace error  easy to use
User Study
Red: reference
Blue: traced by users
4mm trace error  easy to use
(a) CAT
(b) AAMouse (Doppler only)

26. Conclusion Distributed FMCW to support a separate sender and receiver
Optimization framework and algorithm to fuse distance and velocity over time
CAT tracking system
Achieves mm-level accuracy on commodity devices
Future work: develop new applications