北京航空航天大学学报 ›› 2004, Vol. 30 ›› Issue (08): 748-752.

• 论文 • 上一篇    下一篇

基于遗传算法的多模型Kalman滤波算法 及应用研究

王子亮1, 房建成1, 全伟2   

  1. 1. 北京航空航天大学 宇航学院, 北京 100083;
    2. 中国地质大学 地球物理与信息技术学院, 北京 100083
  • 收稿日期:2003-05-23 出版日期:2004-08-31 发布日期:2010-09-21
  • 作者简介:王子亮(1981-),男,江西九江人,硕士生, fancy1981@sina.com.
  • 基金资助:

    国防预研基金资助项目(00J9.2.1HK01); 国防"十五"预研资助项目

Multi-model Kalman filter algorism based on GA

Wang Ziliang1, Fang Jiancheng1, Quan Wei2   

  1. 1. School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;
    2. School of Physical Geography and Information Technology, China University of Geoscience, Beijing 100083, China
  • Received:2003-05-23 Online:2004-08-31 Published:2010-09-21

摘要: 基于长航时无人机惯性/天文/卫星(INS/CNS/GPS)组合导航系统模型,针对复杂环境所引起的系统模型参数变化导致单一固定参数滤波器精度降低问题,提出了一种基于遗传算法的多模型自适应Kalman滤波算法,并与单一模型下的Kalman滤波器方法进行了比较.仿真结果表明,与采用单一模型的Kalman滤波算法相比,该方法不仅能大大提高导航系统的精度和可靠性,而且还可以较好地辨识出组合导航系统惯性器件噪声统计模型参数.

Abstract: A multi-model Kalman filter based on GA(genetic algorism) was designed. When the parameter is imprecise, estimation results of Kalman filter are false.Method used in this paper can estimate the best parameters on line,so as to get true estimation results.At the end of this paper the application to integrated navigation system was studied with simulation. The results of simulation show that as compared with single-model algorithm, this method not only can improve accuracy and reliability of integrated navigation system greatly,but also can identify the noise statistical models of inertial elements in the integrated navigation system relatively well.

中图分类号: 


版权所有 © 《北京航空航天大学学报》编辑部
通讯地址:北京市海淀区学院路37号 北京航空航天大学学报编辑部 邮编:100191 E-mail:jbuaa@buaa.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发