北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (12): 2211-2216.doi: 10.13700/j.bh.1001-5965.2019.0604

• 论文 • 上一篇    下一篇

多表冗余惯导数据融合算法及在自对准中的应用

郭建刚1,2, 陈鹏3, 郑伟1   

  1. 1. 国防科技大学 空天科学学院, 长沙 410073;
    2. 北京航天时代激光导航技术有限责任公司, 北京 100094;
    3. 上海航天技术研究院北京研发中心, 北京 100081
  • 收稿日期:2019-11-26 发布日期:2020-12-28
  • 通讯作者: 郑伟 E-mail:zhengwei@nudt.edu.cn
  • 作者简介:郭建刚,男,博士研究生,高级工程师。主要研究方向:飞行动力学与控制;郑伟,男,博士,教授,博士生导师。主要研究方向:飞行动力学与控制。
  • 基金资助:
    国家自然科学基金(0901010517001)

Data fusion algorithm of multi-sensor redundant inertial navigation and its application in self-alignment

GUO Jiangang1,2, CHEN Peng3, ZHENG Wei1   

  1. 1. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China;
    2. Beijing Aerospace Times Laser Inertial Technology Company, Ltd., Beijing 100094, China;
    3. Shanghai Academy of Spaceflight Technology Beijing R&D Center, Beijing 100081, China
  • Received:2019-11-26 Published:2020-12-28

摘要: 针对多表冗余惯导系统的自对准问题,基于某型三正交两斜置冗余的十表惯导系统,对仪表数据融合算法进行了研究。通过分析和仿真验证了最小二乘估计的数据融合算法在提高系统自对准精度中的有效性,并在静态对准试验的基础上,构造了基于仪表零偏稳定性的加权矩阵。试验结果表明:相较于只使用正交仪表的数据,数据融合可以有效提高系统自对准精度,使斜置冗余仪表的数据得到充分利用,改进算法精度比马尔可夫估计有所提高。

关键词: 多表冗余, 数据融合, 自对准, 最小二乘估计, 惯导系统

Abstract: For the self-alignment problem of the multi-sensor redundant inertial navigation system, the data fusion algorithm of the inertial navigation system is studied based on ten-sensor inertial navigation system which consists of three-orthogonal and two-skew sensors. The validity of the least square estimation data fusion algorithm in improving the self-alignment accuracy of the system is verified through analysis and simulation. Then, a static alignment experiment is conducted and a weighted matrix based on the bias stability is established. The results of the experiment show that, compared with using the data of orthogonal instruments only, the data fusion can effectively improve the self-alignment accuracy of the system and make full use of the data of the skew redundant instruments. The accuracy of the improved algorithm is slightly better than that of Markov estimation.

Key words: multi-sensor redundant, data fusion, self-alignment, least square estimation, inertial navigation system

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