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Title: 3D Indoor State Estimation for RFID-based Motion-capture Systems
Committee:
Dr. Gregory Durgin, ECE, Chair , Advisor
Dr. David taylor, ECE, Co-Advisor
Dr. Ying Zhang, ECE
Dr. Andrew Peterson, ECE
Dr. Mary Ann Weitnauer, ECE
Dr. Billy Kihei, KSU
Abstract: In 3D motion, RMS error before estimation is 71.99 cm, in which 34.99 cm in xy-plane and 62.92 cm along z- axis. After NLE estimation using RF signal combined with IMU data, RMS error in 3D decreases to 31.90 cm, with 22.50 cm in xy-plane and 22.61 cm along z- axis, achieving a factor of 2 enhancement like in 2D estimation. In addition, using RF signal only obtains similar results to using both RF and IMU, i.e., 3D RMS error of 31.90 cm, where 22.48 cm in xy-plane and 22.62 cm along z- axis. Hence, RF signal only is able to achieve fine-scale motion capture in 3D motion like in 2D estimation, which simplifies RFID-based motion capture systems. EKF derives similar estimation results. In addition, ToF based position sensor in tracking achieves comparable and higher accuracy compared to RSS based position sensor from the multipath simulation model, making ToF possible to be applied in fine-scale motion capture and tracking.
