Browsing by Author "Guo, Q."

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Personal inertial navigation system assisted by mems ground reaction sensor array and interface ASIC for GPS-denied environment
(IEEE, 2018-11) Guo, Q.; Deng, W. H.; Bebek, Özkan; Cavusoglu, M. C.; Mastrangelo, C. H.; Young, D. J.; Mechanical Engineering; BEBEK, Özkan
A personal inertial navigation system (PINS) assisted by a microelectromechanical systems (MEMS)-based 13 × 26 ground reaction sensor array (GRSA) and a low-power interface application-specified integrated circuit (ASIC) has been designed and demonstrated for GPS-denied environment. The GRSA operating in a contact mode achieves a sensitivity of approximately 3.7 fF/kPa at each sensor node. An electronic interface system, consisting of a capacitance-to-voltage (C/V) converter followed by a correlated double sampling stage, is designed to convert the GRSA capacitance change to an analog output voltage. The analog output voltage is then digitized by a 12-bit cyclic analog-to-digital converter (ADC). Switch capacitance compensation technique is employed to ensure the ADC performance. The ASIC is fabricated in 0.35-μm CMOS process and dissipates a power of 3 mW. The prototype system incorporates a GRSA, an ASIC, and a commercial nine degreeof-freedom (DOF) inertial measurement unit (IMU) in the heel region of a boot. The GRSA can determine an accurate foot-onground timing based on the pressure profiles detected during walking, thus enabling an accurate position calculation and a precise zero velocity update. Furthermore, a system calibration procedure measures the IMU inherent directional drift and scaling factor errors, and compensates them for the navigation data to achieve a superior performance. The prototype system demonstrates a position accuracy of approximately 5.5 m over a navigation distance of 3100 m. The prototype system also achieves a consistent performance over different field tests with various distances and random paths. System characterization results further indicate a tradeoff between sensor array size and system resolution for a given navigation performance requirement, thus providing a design guideline for future system optimization.
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Personal inertial navigation system employing MEMS wearable ground reaction sensor array and interface ASIC achieving a position accuracy of 5.5m over 3km walking distance without GPS
(IEEE, 2018) Guo, Q.; Deng, W.; Bebek, Özkan; Çavusoglu, C.; Mastrangelo, C.; Young, D.; Mechanical Engineering; BEBEK, Özkan
An accurate personal inertial navigation system under GPS-denied environment is highly critical for demanding applications such as firefighting, rescue missions, and military operations. Location-aware computation for large-area mixed reality also calls for accurate personal position tracking. Position calculation can be accomplished by using an inertial measurement unit (IMU) composed of a 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer. A gyroscope and magnetometer together can provide the orientation information, while the displacement can be obtained by integrating the acceleration data over time. A MEMS-based IMU is attractive for its small size, low power and low cost. However, such devices exhibit a limited accuracy, large offset, and time drift, which can result in an excessive position error over time. To achieve high-performance navigation, it is critical to accurately reset the IMU time-integration during each step when the foot contacts the ground. Furthermore, correcting the IMU inherent inaccuracy, bias, and time drift becomes important for improving system performance.
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A personal navigation system using mems-based high-density ground reaction sensor array and inertial measurement unit
(IEEE, 2015) Guo, Q.; Bebek, Özkan; Çavuşoğlu, M. C.; Mastrangelo, C. H.; Young, D. J.; Mechanical Engineering; BEBEK, Özkan
This paper describes a prototype personal navigation system developed for position tracking under GPS denied environments by employing a commercial inertial measurement unit (IMU) and a MEMS-based ground reaction sensor array (GRSA). The GRSA is used to provide zero-velocity updating for the IMU. A Kalman filter further estimates the IMU output bias during the zero-velocity period, which is then removed from the IMU's subsequent output signals to improve the navigation accuracy. Seven 10-minute square-loop walking tests were performed to achieve an in-plane navigation accuracy ranging from 0.4 meter to 3.4 meter with a vertical position accuracy of approximately 1 meter. A further improved performance over an extended navigation time is expected through enhancing the GRSA sensitivity.