TY - GEN
T1 - A loosely coupled MEMS-SINS/GNSS integrated system for land vehicle navigation in urban areas
AU - Wang, Meiling
AU - Feng, Guoqiang
AU - Yu, Huachao
AU - Li, Yafeng
AU - Yang, Yi
AU - Xiao, Xuan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/25
Y1 - 2017/7/25
N2 - Integrating Micro-Electro Mechanical System-Inertial Measurement Unit (MEMS-IMU) with Global Navigation Satellite System (GNSS) has been a widespread method to achieve cost-effective navigation solution for land vehicles. However, due to the significant and time-varying errors inherent to MEMS inertial sensors, the performance of Strapdown Inertial Navigation System (SINS) based on MEMS-IMU would degrade quickly during the frequent GNSS outages in urban environments. To improve the overall navigation accuracy, this paper proposed a combination of the following two approaches for the loosely coupled integrated system: (1) using the MEMS error coefficients derived from Allan variance analysis for Kalman filter (KF) tuning; and (2) providing additional measurements based on the knowledge of vehicle kinematic features for SINS error correction. In the second method, body velocity constraint and an improved zero velocity updates (ZUPT) method assisted by accelerometers are described. Road tests utilizing an automated vehicle in urban areas demonstrate the effectiveness of the proposed algorithms for reducing the rapid SINS drifts during GNSS outages.
AB - Integrating Micro-Electro Mechanical System-Inertial Measurement Unit (MEMS-IMU) with Global Navigation Satellite System (GNSS) has been a widespread method to achieve cost-effective navigation solution for land vehicles. However, due to the significant and time-varying errors inherent to MEMS inertial sensors, the performance of Strapdown Inertial Navigation System (SINS) based on MEMS-IMU would degrade quickly during the frequent GNSS outages in urban environments. To improve the overall navigation accuracy, this paper proposed a combination of the following two approaches for the loosely coupled integrated system: (1) using the MEMS error coefficients derived from Allan variance analysis for Kalman filter (KF) tuning; and (2) providing additional measurements based on the knowledge of vehicle kinematic features for SINS error correction. In the second method, body velocity constraint and an improved zero velocity updates (ZUPT) method assisted by accelerometers are described. Road tests utilizing an automated vehicle in urban areas demonstrate the effectiveness of the proposed algorithms for reducing the rapid SINS drifts during GNSS outages.
UR - http://www.scopus.com/inward/record.url?scp=85034242171&partnerID=8YFLogxK
U2 - 10.1109/ICVES.2017.7991909
DO - 10.1109/ICVES.2017.7991909
M3 - Conference contribution
AN - SCOPUS:85034242171
T3 - 2017 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2017
SP - 103
EP - 108
BT - 2017 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2017
Y2 - 27 June 2017 through 28 June 2017
ER -