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Dual-antenna GNSS-aided robust MEMS IMU initial alignment under the earth-centered-earth-fixed frame

惯性测量装置 全球导航卫星系统应用 计算机科学 参考坐标系 帧(网络) 全球定位系统 大地测量学 遥感 计算机视觉 电信 地理
作者
S. Zhang,Weiming Tang,Chenlong Deng,Yangyang Li
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:35 (1): 015130-015130
标识
DOI:10.1088/1361-6501/ad0169
摘要

Abstract The micro-electro-mechanical system (MEMS), with its small size, low cost and low power, has attracted widespread attentions in recent year, and the initial alignment of the inertial measurement unit (IMU) is an indispensable step before the navigation computation. In the traditional alignment methods with only the velocity measurements, the yaw angle is subject to divergence under low dynamic scenarios, and the measurements are also susceptible to the external environments. Furthermore, the navigation frame ( n frame) is usually chosen as the reference frame. Although intuitive, it is more computationally complex compared with the earth-centered-earth-fixed frame ( e frame). Therefore, to improve the alignment performance and computational efficiency, a robust MEMS IMU initial alignment method with yaw assistance under the e frame is proposed. In this method, to address the issues of the yaw divergency and computational inefficiency in the traditional methods, the relationship between the yaw angle, derived from dual-antenna global navigation satellite system baseline solution, and IMU error is derived and incorporated into the state space model with e frame as the reference frame. Besides, a robust extended Kalman filtering is adopted to further improve the system’s performance of outlier resistance, whose robust factors are determined by the Institute of Geodesy and Geophysics Ⅲ model. To validate the performance of the proposed method, two field experiments with the velocity of about 0.7 m s −1 and 1.5 m s −1 was conducted, and the collected data were processed for contrastive analysis. The results show that the effectiveness of the proposed method at suppressing yaw angle divergence and improving the alignment accuracy by resisting outliers under low dynamic scenarios compared with the traditional method. Moreover, the elapsed time is reduced by about 11% when the reference frame in data processing is switched from the n frame to the e frame.
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