9轴MEMS-IMU实时姿态估算算法

doi:10.3969/j.issn.1007-2861.2014.01.037

上海大学学报(自然科学版) ›› 2015, Vol. 21 ›› Issue (5): 547-559.doi: 10.3969/j.issn.1007-2861.2014.01.037

9轴MEMS-IMU实时姿态估算算法

张金艺1,2,3, 徐德政2, 李若涵1, 陈兴秀1, 徐秦乐2

1．上海大学特种光纤与光接入网省部共建重点实验室, 上海 200444;
2．上海大学微电子中心, 上海 200444;
3．上海大学教育部新型显示与系统应用重点实验室, 上海 200444

收稿日期:2014-01-02 出版日期:2015-10-30 发布日期:2015-10-30
通讯作者: 张金艺(1965—), 男, 研究员, 博士生导师, 博士, 研究方向为通信类SoC设计与室内无线定位技术. E-mail:zhangjinyi@staff.shu.edu.cn
基金资助:
上海市教委重点学科建设资助项目(J50104); 上海市科委基金资助项目(08706201000, 08700741000)

9-axis MEMS-IMU real-time data fusion algorithm for attitude estimation

ZHANG Jin-yi1,2,3, XU De-zheng2, LI Ruo-han1,CHEN Xing-xiu1, XU Qin-le2

1. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China;
2. Microelectronic Research and Development Center, Shanghai University, Shanghai 200444, China;
3. Key Laboratory of Advanced Displays and System Application, Ministry of Education, Shanghai University, Shanghai 200444, China

Received:2014-01-02 Online:2015-10-30 Published:2015-10-30

摘要/Abstract

摘要： 随着对微机电系统-惯性测量单元(micro-electro-mechanical system-inertial measurement unit, MEMS-IMU)在室内定位、动态追踪等应用领域中的需求日益迫切, 使得具有高精度、低成本和实时性的MEMS-IMU模块设计成为研究热点. 针对MEMS-IMU的核心技术——姿态估算进行研究, 设计了一种基于四元数的9轴MEMS-IMU实时姿态估算算法. 该算法运用分解四元数算法处理加速度和磁感应强度数据, 计算出静态四元数; 通过角速度与四元数的微分关系估算动态四元数; 运用卡尔曼滤波融合动、静态四元数, 进而实现实时姿态估算. 针对分解四元数算法中存在的奇异值问题, 提出了转轴补偿方法对其修正, 以实现全姿态估算; 考虑动态情况下的非线性加速度分量对姿态估算精度的影响, 设计了R自适应卡尔曼滤波器, 以进一步提高姿态估算算法的精度. 验证结果表明, R自适应卡尔曼滤波器能够有效抑制加速度噪声, 提高姿态估算精度; 同时, 转轴补偿-分解四元数算法能够准确估算奇异值点的姿态信息, 并且计算时间仅为原“借角”补偿方法的50%左右, 有效提高了整体算法的实时性.

关键词: 分解四元数算法, 卡尔曼滤波, 奇异值补偿, 微机电系统-惯性测量单元, 姿态估算

Abstract: To meet urgent application demands in indoor location and motion tracking, studies on low-cost high-resolution and real-time micro-electro-mechanical system-inertial measurement unit (MEMS-IMU) have attracted much attention. This paper presents a quaternion-based data fusion algorithm for real-time attitude estimation, including factored quaternion algorithm (FQA) for static attitude estimation, and Kalman filtering fordata fusion. A singularity avoidance method, axis-exchanged compensation, is proposed to modify the FQA, allowing the algorithm to track at all attitudes. An R-adapted module is designed to adjust the Kalman gain, which effectively restrains noise due to dynamic nonlinear acceleration, and improves attitude estimation accuracy. Experimental results show that the R-adapted Kalman filter can accurately estimate attitudes in real-time. Additionally, FQA with an axis-exchanged method has good performance in estimating attitudes of singularity points, and the computational efficiency is higher than a previous method by 50%.

Key words: attitude estimation, factored quaternion algorithm (FQA), Kalman filter (KF), micro-electro-mechanical system-inertial measurement unit (MEMS-IMU), singularity compensation

中图分类号:

张金艺1,2,3, 徐德政2, 李若涵1, 陈兴秀1, 徐秦乐2. 9轴MEMS-IMU实时姿态估算算法[J]. 上海大学学报(自然科学版), 2015, 21(5): 547-559.

ZHANG Jin-yi1,2,3, XU De-zheng2, LI Ruo-han1,CHEN Xing-xiu1, XU Qin-le2. 9-axis MEMS-IMU real-time data fusion algorithm for attitude estimation[J]. Journal of Shanghai University（Natural Science Edition）, 2015, 21(5): 547-559.

参考文献

[1] 蔡春龙, 刘翼, 刘一薇. MEMS 仪表惯性组合导航系统发展现状与趋势[J]. 中国惯性技术学报, 2009, 17(5): 562-567.

[2] Foxlin E. Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter [C]//Virtual Reality Annual International Symposium. 1996: 185-194.

[3] Zhang R, Hoeflinger F, Reindl L. Inertial sensor based indoor localization and monitoring system for emergency responders [J]. IEEE Sensors Journal, 2013, 13(2): 838-848.
[4] Lee J K, Park E J. Minimum-order Kalman filter with vector selector for accurate estimation of human body orientation [J]. IEEE Transactions on Robotics, 2009, 25(5): 1196-1201.

[5] Shuster M D. Filter QUEST or REQUEST [J]. Journal of Guidance, Control, and Dynamics, 2009, 32(2): 643-645.

[6] Liu C, Liu F, Jiang Z, et al. New method for initial orientation estimation in dynamic state using MEMS magnetometers and accelerometers [C]//Modelling, Identification & Control (ICMIC). 2012: 29-34.

[7] Conrado A. Implementation of a quaternion-based Kalman filter for human body motion tracking using MARG sensors [D]. Monterey: Naval Postgraduate School, 2004.

[8] Yun X P, Bachmann E R, McGhee R B. A simplified quaternion-based algorithm for orientation estimation from earth gravity and magnetic field measurements [J]. IEEE Transactions on Instrumentation and Measurement, 2008, 57(3): 638-650.

[9] Sabatelli S, Galgani M, Fanucci L, et al. A double-stage Kalman filter for orientation tracking with an integrated processor in 9-D IMU [J]. IEEE Transactions on Instrumentation and Measurement, 2013, 62(3): 590-598.

[10] 高显忠, 侯中喜, 王波, 等. 四元数卡尔曼滤波组合导航算法性能分析[J]. 控制理论与应用, 2013, 30(2): 171-177.

[11] Brigante C M N, Abbate N, Basile A, et al. Towards miniaturization of a MEMS-based wearable motion capture system [J]. IEEE Transactions on Industrial Electronics, 2011, 58(8): 3234-3241.

9轴MEMS-IMU实时姿态估算算法

9-axis MEMS-IMU real-time data fusion algorithm for attitude estimation

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