A Novel Tightly Coupled Attitude and Heading Measurement Method Based on Full-Sky Polarization Mode for Bionic Navigation System

被引:2
作者
Li, Guangmin [1 ]
Fan, Shiwei [1 ]
Zhang, Ya [1 ]
Wang, Yanyan [2 ]
Wang, Qingxin [1 ]
Yu, Fei [1 ]
Jin, Wenling [3 ]
Wang, Yu [4 ]
机构
[1] Harbin Inst Technol, Sch Instrumentat Sci & Engn, Harbin 150001, Peoples R China
[2] Harbin Engn Univ, Yantai Res Inst, Yantai 264000, Peoples R China
[3] Harbin Inst Technol, Sch Astronaut, Harbin 150001, Peoples R China
[4] Changcheng Inst Metrol & Measurement, Beijing 100095, Peoples R China
基金
中国国家自然科学基金;
关键词
Navigation; Measurement uncertainty; Interference; Vectors; Atmospheric measurements; Scattering; Inertial navigation; Attitude and heading measurement; polarization navigation; polarization-based tightly coupled (TC) model; robust fusion mechanism; sequential filter; SKYLIGHT POLARIZATION;
D O I
10.1109/TIM.2024.3449953
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The bionic navigation system composed of polarization sensor (PS)-aided inertial measurement unit could provide high-accuracy attitude and heading for unmanned vehicles in GNSS rejection or magnetic interference environments. However, the existing loosely coupled (LC) method is vulnerable to occlusion interference and atmospheric depolarization. Therefore, this article proposes the tightly coupled (TC) attitude and heading measurement method based on full-sky polarization mode. First, the TC system model is constructed, in which the sun azimuth error and zenith error are added to the state vector, and the angle of polarization (AOP) deviation of multiple observation channels are taken as the measurement. On this basis, a robust fusion mechanism based on the sequential cubature Kalman filter is proposed. And the linear gravity measurement update and the nonlinear polarization measurement update are carried out in turn to improves the system robustness. In the rotation test, the slight interference vehicle test, and the serious interference vehicle test, the heading root-mean-square error (RMSE) of the proposed method compared with that of the LC method are reduced by 32.5%, 32.3%, and 70.2%, respectively, which verifies the great advantages in practical unmanned driving applications.
引用
收藏
页数:11
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