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BatTracker: High Precision Infrastructure-free Mobile Device Tracking in Indoor Environments

BatTracker: High Precision Infrastructure-free Mobile Device Tracking in Indoor Environments. Bing Zhou 1 , Mohammed Elbadry 2 , Ruipeng Gao 3 , Fan Ye 1 1 ECE Department, Stony Brook University 2 CS Department, Stony Brook University

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BatTracker: High Precision Infrastructure-free Mobile Device Tracking in Indoor Environments

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  1. BatTracker: High Precision Infrastructure-free Mobile Device Tracking in Indoor Environments Bing Zhou1, Mohammed Elbadry2, Ruipeng Gao3, Fan Ye1 1 ECE Department, Stony Brook University 2 CS Department, Stony Brook University 3 School of Software Engineering, Beijing Jiaotong University ACM SenSys 2017 Delft, The Netherlands

  2. Motivation - Motion Tracking Video Gaming Virtual Reality Health Rehabilitation

  3. Current Approaches • Vision based • Special hardware • Lighting condition • Computationally heavy • Privacy issues Oculus VR Microsoft XBOX360

  4. Current Approaches • RF signals based • Wi-Fi, RFID • Limited accuracy due to the high propagation speed • mmWave (e.g., 60GHz) • High accuracy while hardware is not available in most existing devices Image from Google Project Soli

  5. Acoustic Approach • Acoustic signal • Low propagation speed • High ranging accuracy • Less privacy issue • No image/video data captured • Light computation • Orders of magnitude less compared to vision method • Existing hardware • Almost all smart devices have speakers and microphones

  6. BatTracker Design Speaker & Microphone Distance measurements Distance to reference objects Tracking these distances

  7. Acoustic Sensing 1ms Emitting signal: Frequency 17KHz Duration 1ms Interval 30ms Hanning window 30ms Cross-correlate Time of arrival (Distance) 30ms Direct Path Amplitude Echo Echo Received signal: Noise removed Echo Frequency shift (Velocity) STFT STFT STFT

  8. Track Initiation Track Generation Track Association Final Selection 5 5 4 3 2 3 1 1 1 Correlate distance measurements with accelerometer data

  9. Challenges Distance Measurements False track divergence Track crossing E. D. C. B. A. P2 P1 P2 P4 P2 P3 P1 P1 P2 P1 P3 X P3 P2 P3 P1 P5 P3 Y P4 P5 Inertial sensors can help! Missing data Tracks diverge after merge Time Naïve method 2: Continuous velocity Naïve method 3: Reliable measurements Naïve method 1: Continuous movement Assumptions:

  10. Tracking Framework Overview Track Initiation Distance Measurements Current Tracks Multi-Hypothesis Tracking Motion Model Linear Acceleration Gyroscope Track Updating Observation Model Probabilistic Data Association Distance Candidates Track Splitting Amplitude Candidates Weighting and Resampling Doppler Shift Candidates Time Track Pruning Track Estimation

  11. Multi-hypothesis Tracking Particle Filter Algorithm Track Pruning Track Splitting Initial Track Track Update Current state Landmark Predicted state from inertial data Validated measurements

  12. Probabilistic Data Association • Measurement likelihood: ω1 ω0 Track Update • Incorporate velocity (Doppler shift) and amplitude: ω2 ω3 Echoes with similar distance usually have different velocity along different direction Amplitude tends to be continuous for echoes from same object • Data Missing Probability: PM(t) heavily related to the phone pose (holding gesture), We increase PM(t) when any two tracks are close to each other.

  13. Evaluation - Setup

  14. Evaluation - Ranging Accuracy 0.5m, 1m, 1.5m, 2m, 2.5m, 3m, 30 measurements at each location. ~1cm error at 90%, Maximum error is ~2cm. Robust to ambient noise.

  15. Evaluation - Inertial, Doppler and Ranging

  16. Evaluation – 2D Tracking Accuracy Sub-cm accuracy for 2D tracking, even higher than random ranging accuracy test! Smoothing nature of our algorithm helps remove outliers, and smooth the track. CAT triangulates the device position from distances to multiple speakers, which enlarges the error. AAMouse has accumulated error, while CAT and BatTracker do not have. CAT: Wenguang Mao, Jian He, and Lili Qiu. “CAT: high-precision acoustic motion tracking.” [MobiCom 2016] AAMouse: Sangki Yun, Yi-Chao Chen, and Lili Qiu. “Turning a mobile device into a mouse in the air.” [MobiSys 2015]

  17. Evaluation – 3D Tracking Accuracy

  18. Evaluation – Different Algorithms Tracking comparison: More drawing examples:

  19. Evaluation – Efficiency • Allocated memory and CPU usage on smartphone • Tracking error and number of particles

  20. Limitation • Limited tracking range • Current design has a range of . • Device holding gesture • Quality omni-directional speaker/microphone may help. • Reference Objects • Require clean walls, large furniture such as closets, cabinets, and tables. • Track loss problem • As probabilistic algorithms are used, we still have chances for trace losing.

  21. Future work • Fast track recovery • Design a mechanism for automatic track loss detection and recovery. • Utilize all the available objects • Leverage all stable reflections • Customized hardware • Customized omnidirectional, high-sensitivity microphones • Different devices • More comprehensive tests on different smart phones

  22. Thank You.

  23. Back Up Slides

  24. Evaluation – Data Missing Probability Wall

  25. Evaluation – Impact of Initiation Error

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