宇航学报

宇航学报 ›› 2022, Vol. 43 ›› Issue (9): 1246-1256.doi: 10.3873/j.issn.1000-1328.2022.09.012

• 制导、导航、控制与电子 • 上一篇    下一篇

高超声速机动目标天基跟踪鲁棒性滤波方法

魏世君，翟光，孙一勇，毕幸子，汪宏昇   

  1. 1. 北京理工大学宇航学院，北京 100081；2. 中国科学院微小卫星创新研究院，上海 201203；3. 中国运载火箭技术研究院研究发展部，北京 100076
  • 收稿日期:2022-03-17 修回日期:2022-06-18 出版日期:2022-09-15 发布日期:2022-09-15
  • 基金资助:
    国家自然科学基金（11872109）

Robust Filtering and Space based Tracking Method for Hypersonic Maneuvering Target

WEI Shijun, ZHAI Guang, SUN Yiyong, BI Xingzi, WANG Hongsheng   

  1. 1. School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China; 2. Innovation Academy  for Microsatellite of Chinese Academy of Sciences, Shanghai 201203, China; 3. Department of Research and Development, China Academy of Launch Vehicle Technology, Beijing 100076, China
  • Received:2022-03-17 Revised:2022-06-18 Online:2022-09-15 Published:2022-09-15

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摘要： 为解决高超声速滑翔飞行器(HGV)机动飞行过程中的跟踪问题，提出了一种基于机动观测器补偿的鲁棒扩展卡尔曼滤波方法。针对传统卡尔曼滤波器由于模型误差而无法稳定跟踪的问题，首先建立了HGV动力学模型和天基红外测量模型；随后，设计机动观测器对未知气动加速度进行在线估计；在此基础上，利用机动估计对扩展卡尔曼滤波中的预测步骤进行修正，克服了因模型误差而导致的滤波发散问题；最后，考虑到机动观测器的时延误差，在扩展卡尔曼滤波迭代过程中引入次优渐消因子，提高了滤波跟踪的鲁棒性。与强跟踪滤波、扩维卡尔曼滤波、交互多模型滤波等典型跟踪方法相比，所提方法可在不具备目标机动先验信息的情况下，以较低的计算耗时取得良好的稳定性和跟踪精度。

关键词: 高超声速滑翔飞行器, 机动目标跟踪, 状态估计, 机动观测器, 卡尔曼滤波

Abstract: To solve the tracking problem of hypersonic glide vehicle (HGV) during maneuvering flight, a robust extended Kalman filter based on maneuvering observer compensation is proposed. Aiming at the problem that the traditional Kalman filter cannot track stably due to model error, firstly, the HGV dynamic model and the space based infrared measurement model are established. Secondly, a maneuvering observer is designed to estimate the unknown aerodynamic acceleration online. On this basis, maneuver estimation is used to modify the prediction step of extended Kalman filter to overcome the problem of filter divergence caused by model error. Finally, considering the time delay error of the maneuver observer, a suboptimal fading factor is introduced into the iterative process of the extended Kalman filter to improve the robustness of the filter tracking. Compared with typical tracking methods such as strong tracking filter, augmented state Kalman filter and interactive multiple model filter, the proposed method can achieve good stability and tracking accuracy with lower computing time without prior information of maneuvering target.

Key words: Hypersonic glide vehicle, Maneuvering target tracking, State estimate, Maneuver observer, Kalman filter

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