中国空间科学技术

中国空间科学技术 ›› 2024, Vol. 44 ›› Issue (1): 114-123.doi: 10.16708/j.cnki.1000-758X.2024.0013

• 论文 • 上一篇    下一篇

基于自适应中心误差熵的相对导航滤波算法

张爽，曹璐，杨宝健，季明江   

  1. 1军事科学院 国防科技创新研究院，北京100171
    2陆军工程大学，石家庄050003
  • 出版日期:2024-02-25 发布日期:2024-02-01

Relative navigation filtering algorithm based on adaptive centered error entropy

ZHANG Shuang，CAO Lu，YANG Baojian，JI Mingjiang   

  1. 1National Innovation Institute of Defense Technology，Chinese Academy of Military Science，Beijing 100171，China
    2Army Engineering University of PLA，Shijiazhuang 050003，China
  • Published:2024-02-25 Online:2024-02-01

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摘要： 基于中心误差熵准则的卡尔曼滤波算法在非高斯噪声下具有强鲁棒性，但仍面临权值系数如何选择的难题。针对上述问题，提出一种变权值系数的自适应中心误差熵卡尔曼滤波算法。该算法将误差矢量引入权值系数更新函数，增强了代价函数对误差的敏感性，提高了算法的滤波精度。将其应用于编队卫星相对导航，仿真结果表明，相比于卡尔曼滤波算法和中心误差熵卡尔曼滤波算法，所提算法在处理线性非高斯系统的状态估计问题时表现出了良好的滤波性能，具有更高的滤波精度和对非高斯噪声更强的抑制能力。

关键词: 卡尔曼滤波, 自适应中心误差熵, 非高斯噪声, 相对导航, 状态估计

Abstract:  The centered error entropy Kalman filter algorithm has strong robustness under nonGaussian noise，but it still faces the challenge of how to choose the weight coefficient.To solve this issue，an adaptive centered error entropy Kalman filter algorithm with a variable weight coefficient was proposed.The weight coefficient is adaptively adjusted according to the error vector，which increases the sensitivity of the cost function to the error and improves the filtering accuracy.By applying to relative navigation of formation satellites，the simulation results demonstrate that the proposed algorithm outperforms the Kalman filter and the centered error entropy Kalman filter algorithms when dealing with the state estimation problem in the linear non-Gaussian system.A higher filtering accuracy and a stronger ability to suppress non-Gaussian noise are presented.

Key words:  , Kalman filter；adaptive centered error entropy；non-Gaussian noises；relative navigation；state estimation