›› 2013, Vol. 33 ›› Issue (2): 67-71.doi: 10.3780/j.issn.1000-758X.2013.02.011

• 技术交流 • 上一篇    下一篇

基于自适应联邦滤波的卫星姿态确定

李鹏1, 唐健1, 段广仁2, 宋申民2   

  1. (1 湘潭大学信息工程学院智能计算与信息处理教育部重点实验室,湘潭 411105)(2 哈尔滨工业大学航天学院,哈尔滨 150001)
  • 收稿日期:2012-04-14 修回日期:2012-10-23 出版日期:2013-04-25 发布日期:2013-04-25
  • 作者简介:李鹏 1978年生,2010年获哈尔滨工业大学控制科学与工程专业博士学位,讲师。研究方向为非线性滤波,导航制导与控制。
  • 基金资助:

    863国家高科技计划(2009AA***5004),湖南省教育厅一般项目(11c1217),湖南省科技厅支撑项目(2012GK3141)资助项目

Satellite Attitude Determination Based on the Adaptive Federated Kalman Filter

LI  Peng1, TANG  Jian1, DUAN  Guang-Ren2, SONG  Shen-Min2   

  1. (1 Key Laboratory of Intelligent Computing & Information Processing, Ministry of Education, College of Information and Technology, Xiangtan University,Xiangtan 411105)(2 School of Astronautics, Harbin Institute of Technology, Harbin 150001)
  • Received:2012-04-14 Revised:2012-10-23 Online:2013-04-25 Published:2013-04-25

摘要: 卡尔曼滤波采用常值量测噪声协方差阵,当量测噪声统计特性发生变化时,易导致估计误差增大,甚至滤波发散。针对该问题,在联邦卡尔曼滤波子系统中采用自适应卡尔曼滤波,形成自适应联邦卡尔曼滤波算法,新算法采用模糊推理系统实时调整量测噪声协方差阵的加权系数,使模型量测噪声逐渐逼近真实噪声水平。将该算法应用于多传感器卫星姿态确定系统,仿真结果验证了算法的有效性。

关键词: 自适应卡尔曼滤波, 联邦滤波, 多传感器系统, 姿态确定, 卫星

Abstract: Standard Kalman filter adopts constant covariance of measurement noise. When statistical characteristics of measurement noise changes, estimation error increases, which results in filtering divergence. An adaptive federated Kalman filter was proposed with fuzzy adaptive Kalman filter but not Kalman filter in the subsystem of federated Kalman filter, and the weighted coefficient of covariance matrix was adjusted by fuzzy inference algorithm real-timely. It made the measurement noise of the dynamic equation close to the truth level. When it is applied to multi-sensor attitude determination systems, simulation results demonstrate the true effectiveness of the proposed algorithm.

Key words: Self-adaptive Kalman filter, Federated Kalman filter, Multi-sensor system, Attitude determination, Satellite