Doctor of Philosophy

dissertationGlobal navigation satellite systems are the most frequently used position tracking tools. However, there are environments where satellite signals cannot be obtained. Therefore, a personal position tracking system in such environments is highly desirable, especially for critical applications such as firefighting, rescue missions, and military operations. Inertial measurement unit (IMU)-based position calculation is the only form of position tracking method that does not require an external reference and is therefore preferred. Microelectromechanical systems (MEMS)-based IMUs are low power, low cost, and small sized. These features are ideal for implementing personal inertial navigation system (PINS). However, MEMS-based IMUs typically have low accuracy, large offset, and time drift, which can lead to a drastic position error over a short navigation time. It has been demonstrated that during human locomotion the foot heel is close to stationary within a short period of time when the foot makes contact to the ground. This stationary period can be employed to reset the IMU output time integration to suppress position error accumulation. Traditional methods employ the IMU vector output to measure the foot-onground (FoG) timing. However, the FoG window determined by these techniques typically exhibits a limited accuracy. In order to solve these challenges, a high-density ground reaction sensor array (GRSA) is proposed to accurately detect the foot ground reaction pressure profile, from which the gait phases can be accurately detected. A robust flexible 13 X 26 GRSA with an improved linearity has been designed and iv fabricated. The GRSA is interfaced by a custom-designed application-specific integrated circuit (ASIC). The ASIC is designed to scan through the GRSA and convert each sensor cell capacitance change to digitized voltage change, from which the ground reaction pressure profile can be obtained. The fabricated GRSA and ASIC with a commercial offthe- shelf 9-axis IMU are assembled into the heel region of a boot for performance evaluation. The prototype GRSA-assisted PINS demonstrates a 5.5m navigation error over 3100m navigation distance, which presents approximately 2.3X better performance over around 2.5X longer navigation distance than the performance reported to date