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Performance analysis of the GNSS / MEMS-IMU / on-Board vehicle sensor / magnetometer-based positioning system during GNSS signal blockage
Recently, as a result of developments in microelectromechanical systems (MEMS) technology, various studies have been conducted to perform positioning by combining low-cost MEMS-based IMUs and the GNSS. The advantage of MEMS IMU is its low cost; however, its limitation is that the navigation error rapidly increases when disconnected from the GNSS. Therefore, precise positioning is difficult in tunnels or urban environments, where GNSS signals are unreliable. For this reason, additional sensors are needed. In this study, we intend to improve the accuracy of existing GNSS/IMU couplings using internal sensors and a magnetometer (MAG) attached to a vehicle.
In this study, a positioning algorithm is developed based on the extended Kalman filter using on-board vehicle sensors and a MAG in addition to GNSS/IMU. A wheel speed sensor (WSS) and yaw rate sensor (YRS) were used as the on-board vehicle sensors. Experimental data were acquired and performance was analyzed. The results show that the GNSS/MEMS-IMU/WSS/YRS/MAG combination has the most stable positional accuracy, with a horizontal deviation of about 3.6 m observed in 10 zones of 30-second GNSS signal blockage. The performance was not significantly improved by adding the YRS; however, when the WSS and the MAG were used, the performance was greatly improved in the zones with GNSS signal blockage.
Performance analysis of the GNSS / MEMS-IMU / on-Board vehicle sensor / magnetometer-based positioning system during GNSS signal blockage
Recently, as a result of developments in microelectromechanical systems (MEMS) technology, various studies have been conducted to perform positioning by combining low-cost MEMS-based IMUs and the GNSS. The advantage of MEMS IMU is its low cost; however, its limitation is that the navigation error rapidly increases when disconnected from the GNSS. Therefore, precise positioning is difficult in tunnels or urban environments, where GNSS signals are unreliable. For this reason, additional sensors are needed. In this study, we intend to improve the accuracy of existing GNSS/IMU couplings using internal sensors and a magnetometer (MAG) attached to a vehicle.
In this study, a positioning algorithm is developed based on the extended Kalman filter using on-board vehicle sensors and a MAG in addition to GNSS/IMU. A wheel speed sensor (WSS) and yaw rate sensor (YRS) were used as the on-board vehicle sensors. Experimental data were acquired and performance was analyzed. The results show that the GNSS/MEMS-IMU/WSS/YRS/MAG combination has the most stable positional accuracy, with a horizontal deviation of about 3.6 m observed in 10 zones of 30-second GNSS signal blockage. The performance was not significantly improved by adding the YRS; however, when the WSS and the MAG were used, the performance was greatly improved in the zones with GNSS signal blockage.
Performance analysis of the GNSS / MEMS-IMU / on-Board vehicle sensor / magnetometer-based positioning system during GNSS signal blockage
Lee, Yong (author) / Kwon, Jay Hyoun (author)
International Journal of Urban Sciences ; 23 ; 434-443
2019-07-03
10 pages
Article (Journal)
Electronic Resource
English
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