重力/地磁辅助的欧拉角无迹卡尔曼滤波姿态估计

贾瑞才

doi:10.3788/OPE.20142212.3280

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重力/地磁辅助的欧拉角无迹卡尔曼滤波姿态估计

微纳技术与精密机械 | 更新时间：2020-08-13

- 重力/地磁辅助的欧拉角无迹卡尔曼滤波姿态估计
- Attitude estimation base on gravity/magnetic assisted Euler angle UKF
- 光学精密工程 2014年22卷第12期页码：3280-3286
- 作者机构：
  
  中国电子科技集团公司第五十四研究所,河北石家庄,050081
- 作者简介：
  
  [ "贾瑞才(1986-), 男, 山东临沂人, 博士后, 工程师, 2012年于哈尔滨工程大学获得博士学位, 主要从事组合导航, 卫星导航, 惯性导航算法研究.E_mail:jiaruicai@126.com" ]
- 基金信息：
  
  国家863高技术研究发展计划资助项目(No.2012AA12A206)();中国博士后科学基金资助项目(No.2013M541202)()
- DOI：10.3788/OPE.20142212.3280
  中图分类号： TP301.6;V448.22
- 收稿日期：2014-03-09，
  
  修回日期：2014-04-10，
  
  纸质出版日期：2014-12-25
- 稿件说明：
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贾瑞才,. 重力/地磁辅助的欧拉角无迹卡尔曼滤波姿态估计[J]. 光学精密工程, 2014,22(12): 3280-3286

JIA Rui-cai,. Attitude estimation base on gravity/magnetic assisted Euler angle UKF[J]. Editorial Office of Optics and Precision Engineering, 2014,22(12): 3280-3286
贾瑞才,. 重力/地磁辅助的欧拉角无迹卡尔曼滤波姿态估计[J]. 光学精密工程, 2014,22(12): 3280-3286 DOI： 10.3788/OPE.20142212.3280.

JIA Rui-cai,. Attitude estimation base on gravity/magnetic assisted Euler angle UKF[J]. Editorial Office of Optics and Precision Engineering, 2014,22(12): 3280-3286 DOI： 10.3788/OPE.20142212.3280.

摘要

为了克服应用扩展卡尔曼滤波(EKF)的姿态估计算法的线性化误差问题

提出了一种基于重力/地磁辅助的欧拉角无迹卡尔曼滤波(UKF)姿态估计算法来提升低成本微机电系统(MEMS)的姿态测量精度.应用重力与地磁数据抑制了MEMS姿态误差快速发散问题;将欧拉角作为状态

应用四元数完成时间更新过程中的姿态更新

避免了四元数作为状态的规范化问题及欧拉角姿态更新精度低的问题;由于UKF滤波器不存在线性化误差

故其具有更好的稳定性和姿态估计精度.应用实际MEMS数据开展的算法验证实验显示:与EKF姿态估计算法相比

提出的UKF姿态估计算法得到的俯仰与横滚角精度提高了近20%

航向角精度提高了12.1%.结果表明:本文提出算法的精度更高;然而由于UKF算法对状态协方差估计不足

其收敛时间有所增加.

Abstract

To overcome the linearizated errors from the attitude estimation algorithm based on Extended Kalman Filter (EKF)

a new attitude estimation algorithm based on gravity/magnetic assisted Euler angle Unscented Kalman Filter (UKF)algorithm was proposed to improve the attitude measuring accuracy of a low cost Micro-electro-mechanic System(MEMS). The gravity and magnetic data were used to inhibit the rapid divergence of attitude error for the MEMS. The Euler angles were taken as UKF states and the quaternion was used to calculate the attitude in time update

so that the algorithm avoids the quaternion standardized problem and solves the low attitude accuracy of Euler angles. Without linearization errors of the UKF

it has better stability and attitude estimation accuracy. By taking measured MEMS Inertial Measurement Unit (IMU)data for experiments

the results show that the measuring accuracy of pitch and roll angles by proposed algorithm has improved nearly 20 percent respectively and the heading accuracy improved by 12.1 percent as compared with that of the attitude estimation algorithm based on the EKF. It concludes that the proposed method has better precision. However

the convergence time of UKF has increased due to the insufficient estimation for state variances.

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Keywords

references

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