期刊文献+

基于多传感器的智能车辆姿态解算方法 被引量:5

Intelligent Vehicle Attitude Calculation Method Based on Multi-Sensor
下载PDF
导出
摘要 针对智能车辆主动环境感知的需求,提出了一种采用三轴加速度计、三轴磁强计和三轴陀螺仪组合进行车辆姿态解算的方法。首先以旋转矢量法为陀螺仪的车辆姿态解算方法,作为扩展卡尔曼滤波的状态方程,用于车辆姿态的预测;其次以高斯牛顿法为加速度计和磁强计的车辆姿态解算方法,作为扩展卡尔曼滤波的观测方程,用于车辆姿态校正;然后在此基础上构建扩展卡尔曼滤波传播方程,采用扩展卡尔曼滤波进行多传感器信息融合,得到车辆的姿态解算结果;最后通过构建实车测试环境对解算方法有效性进行验证。实验结果表明,通过基于多传感器的车辆姿态解算方法解算得到的车辆姿态角稳定、准确,能够满足智能车辆行为参数估计的实际需求。 To meet the demand of intelligent vehicle active environment perception,a method for vehicle attitude calculation is presented which is based on the combined use of the three-axis accelerometer,three-axis magnetometer and three axis gyroscope.Firstly,the rotating vector method is taken as vehicle attitude calculation method of gyroscope,which can be used to predict the attitude of the vehicle as the state equation of extended Kalman filter.Secondly,the Gauss Newton method is taken as vehicle attitude calculation method of accelerometer and magnetometer,which can be used to correct vehicle attitude as observation equation of extended Kalman filter.Thirdly,the propagation equation of extended Kalman filter is constructed,and the calculation results of vehicle attitude are obtained by multi-sensor information fusion.Finally,the effectiveness of the calculation method is verified by building a real test environment.The experimental results show that the method proposed can obtain the vehicle attitude angle stably and accurately,which is able to meet the practical requirements for the parameter estimation of intelligent vehicle behavior.
出处 《测控技术》 CSCD 2016年第9期15-19,24,共6页 Measurement & Control Technology
基金 国家自然科学基金项目(51278058) 国家物联网重大示范工程专题研究项目(2012-364-812-105) 交通部基础应用项目(2015319812060)
关键词 智能车辆 姿态解算 多传感器 扩展卡尔曼滤波 intelligent vehicle attitude calculation multiple sensors extended Kalman filtering
  • 相关文献

参考文献11

  • 1Monreal C O, Rossetti R J F. Human factors in intelligent ve- hicles[ guest editorial ] [ J ]. IEEE Transactions on Intelligent Transportation Systems ,2014,15 (4) : 1734 - 1737.
  • 2Chen L, Li Q, Li M, et al. Design of a multi-sensor coopera- tion travel environment perception system for autonomous ve- hicle[ J ]. Sensors ,2012,12 (9) : 12386 - 12404.
  • 3Zhang Z,Huang K Q,Tan T N,et al. Trajectory series analy- sis based event rule induction for visual surveillance [ C ]// 2007 IEEE Conference on Computer Vision and Pattern Rec- ognition. 2007 : 1 - 8.
  • 4Piciarelli C, Micheloni C, Foresti G L. Anomalous trajectory patterns detection [ C]//19th International Conference on Pattern Recognition. 2008 : 1 - 4.
  • 5魏小峰,耿则勋,娄博,宋向.空间目标三维姿态估计方法[J].武汉大学学报(信息科学版),2015,40(1):96-101. 被引量:12
  • 6Sanz R, Rodenas L, Garcia P, et ah Improving attitude esti- mation using inertial sensors for quadrotor control systems [ C ]//2014 International Conference on Unmanned Aircraft Systems (ICUAS). 2014 : 895 - 901.
  • 7刘凯,梁晓庚.基于陀螺仪和磁强计的姿态解算方法研究[J].计算机仿真,2014,31(5):39-41. 被引量:12
  • 8Thepvilojanapong N, Sugo K, Namiki Y, et al. Recognizing bicycling states with hmm based on accelerometer and mag- netometer data[ C ]//2011 Proceedings of SICE Annual Con- ference ( SICE ). 2011 : 831 - 832.
  • 9Kirkko-Jaakkola M, Collin J, Takala J. Bias prediction for MEMS gyroscopes [ J ]. IEEE Sensors Journal, 2012,12 ( 6 ) : 2157 -2163.
  • 10Ozyagcilar T. Implementing a tilt-compensated ecompass u- sing accelerometer and magnetometer sensors [ Z ]. Freescale Semiconductor,2011.

二级参考文献16

  • 1李泽民,段凤阳,李赞平.基于MEMS传感器的数字式航姿系统设计[J].传感器与微系统,2012,31(6):94-96. 被引量:14
  • 2李荣冰,刘建业,曾庆化,华冰.基于MEMS技术的微型惯性导航系统的发展现状[J].中国惯性技术学报,2004,12(6):88-94. 被引量:91
  • 3国际地磁与超高层大气物理学协会第5分会地磁场模型工作组,张素琴.第10代国际地磁参考场[J].世界地震译丛,2005,36(5):67-75. 被引量:2
  • 4高昕,王建立,周泗忠,黄惠明,熊仁生.空间目标光度特性测量方法研究[J].光电工程,2007,34(3):42-45. 被引量:14
  • 5Lai Ying Chih,Jan Shau Shiun,Hsiao Fei Bin.Development of alow-cost attitude and heading reference system using a three-axisrotating platform[J].Sensors,2010,10:2472-2491.
  • 6Wang Mei,Yang Yunchun,Hatch Ronald R,et al.Adaptive filterfor a miniature MEMS based attitude and heading reference sys-tem[C]∥IEEE Position Location and Navigation Symposium,Monterey,CA,USA,2004:193-200.
  • 7张永生,贲进,童晓冲.地球空间信息球面离散网格[M].北京:科学出版社,2007:9-19.
  • 8Weeden B C, Cefola P J. Computer Systems and Algorithms for Space Situational Awareness: Histo- ry and Future DevelopmentEC]. The 12th Interna- tional Space Conference of Pacific-basin Societies, Quebec, Canada, 2010.
  • 9Sahr K, White D, Kimerling A J. Geodesic Discrete Global Grid Systems [J]. Cartography and Geo- graphic Information Science, 2003, 30(2): 121- 134.
  • 10Benvenuto F, Camera A L, Theys C, et al. The Study of an Iterative Method for the Reconstruction of Images Corrupted by Poisson and Gaussian Noise [J]. Inverse Problems, 2008, 24(3) : 1-20.

共引文献51

同被引文献34

引证文献5

二级引证文献18

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部