摘要
提出了一种融合捷联惯导、轮式里程计和气压高度计数据的高精度车载自主导航滤波算法。首先,采用滤波鲁棒性更好的状态变换卡尔曼滤波器(ST-EKF)替代传统的扩展卡尔曼滤波器(EKF),对INS/轮式里程计信息进行组合滤波。其次,针对车载惯性/里程计组合导航系统中高程定位误差易发散的问题,引入气压高度计的量测信息对系统的高程通道进行阻尼,提高高程定位精度。两组长行驶里程的高精度光纤陀螺惯性测量单元/轮式里程计/气压高度计的车载导航实验数据的事后处理表明,所提出算法在不同的行驶环境下均具有较高的导航定位精度,相比基于EKF的惯性/里程计组合导航算法,水平定位精度提高20%以上,并且有效减缓了高程定位误差的发散趋势。
A high-precision Kalman filtering algorithm for land vehicle self-contained navigation based on data fusion of strapdown inertial navigation system(SINS),wheel odometer(OD)and barometer(Baro)is proposed.Firstly,the state transform Kalman filter(ST-EKF)with better filtering robustness is used to replace the traditional extended Kalman filter(EKF)for the combined filtering of INS/wheel odometer information.Secondly,aiming at the problem that the height error is easy to diverge in the land vehicle SINS/OD integrated navigation system,the barometer is used to damp the height channel to improve the positioning accuracy.Simulation by post-processing two groups of the long distance fiber optic gyroscope inertial measurement unit(IMU)/odometer/barometer experimental data shows that the algorithm has high positioning accuracy in different driving environments.Meanwhile,compared with the integrated navigation algorithm of SINS/Odometer based on EKF,the proposed algorithm improves the horizontal positioning accuracy by more than 20%,and effectively slow down the divergence trend of height positioning error.
作者
杜学禹
王茂松
崔加瑞
吴文启
何晓峰
DU Xueyu;WANG Maosong;CUI Jiarui;WU Wenqi;HE Xiaofeng(College of Intelligence Science and Technology,National University of Defense Technology,Changsha 410073,China)
出处
《中国惯性技术学报》
EI
CSCD
北大核心
2021年第1期55-61,共7页
Journal of Chinese Inertial Technology
基金
国防科技大学科研项目(ZK19-26)。
