Undergraduate Research and Innovation Scholar
WiTrack 3.0: Through-Wall Detection Using Multi-Antenna Arrays
- Artificial Intelligence and Machine Learning
This project builds on previous technology in through-wall RF-based localization systems that can track user motion without having users carry additional devices. While previous devices have used multiple widely spaced transmit antennas and a single receive antenna, this work aims to reduce system size to fit on a smaller footprint while maintaining existing signal-to-noise ratio and tracking abilities. This can be achieved by using multi-antenna array design techniques that use up to three receive antennas. To
process the increased amount of receive data, we must develop a new FPGA architecture and a redesigned FPGA-to-host communication
protocol. Ultimately, the system will provide a high-accuracy motiontracking system for multiple applications in a smaller device footprint with improved transmit power efficiency.
I decided to participate in SuperUROP to apply the knowledge I have gained during university to real-world research problems. I chose this project as I have experience applying recently conceived adaptive signal processing techniques to analyze EEG/ECG data. I would like to learn to apply machine learning techniques to biological data. I am keen to apply my knowledge to acquire useful results and, if possible, publish the findings in a paper.