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This research presents BatTracker, an innovative infrastructure-free mobile tracking system utilizing acoustic signals for high-precision indoor positioning. The system leverages the smartphone's speaker and microphone to measure distances to known reference objects, and then tracks these distances using acoustic sensing techniques. The approach exhibits low computational overhead, enhanced privacy measures, and superior accuracy compared to existing methods. Challenges such as false track divergence and missing data are addressed through a sophisticated multi-hypothesis tracking framework that integrates sensor data and probabilistic models. The system demonstrates sub-centimeter tracking accuracy in both 2D and 3D scenarios, with promising potential for applications in motion tracking, video gaming, virtual reality, and health rehabilitation. Future work includes optimizing track recovery mechanisms, customizing hardware for better performance, and expanding testing on diverse smart devices.
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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
Motivation - Motion Tracking Video Gaming Virtual Reality Health Rehabilitation
Current Approaches • Vision based • Special hardware • Lighting condition • Computationally heavy • Privacy issues Oculus VR Microsoft XBOX360
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
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
BatTracker Design Speaker & Microphone Distance measurements Distance to reference objects Tracking these distances
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
Track Initiation Track Generation Track Association Final Selection 5 5 4 3 2 3 1 1 1 Correlate distance measurements with accelerometer data
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:
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
Multi-hypothesis Tracking Particle Filter Algorithm Track Pruning Track Splitting Initial Track Track Update Current state Landmark Predicted state from inertial data Validated measurements
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.
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.
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]
Evaluation – Different Algorithms Tracking comparison: More drawing examples:
Evaluation – Efficiency • Allocated memory and CPU usage on smartphone • Tracking error and number of particles
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.
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
