吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (4): 1482-1489.doi: 10.13229/j.cnki.jdxbgxb20200374

• 通信与控制工程 • 上一篇    

非合作目标强跟踪容积卡尔曼滤波运动状态估计

谢少彪1,2(),张宇3,温凯瑞3,张硕3,刘宗明3(),齐乃明1   

  1. 1.哈尔滨工业大学 航天学院，哈尔滨 150090
    2.上海航天技术研究院，上海 201109
    3.上海航天控制技术研究所，上海 201109
  • 收稿日期:2020-05-28 出版日期:2021-07-01 发布日期:2021-07-14
  • 通讯作者: 刘宗明 E-mail:boyish9747@outlook.com;zongmingliu@163.com
  • 作者简介:谢少彪（1980-），男，研究员，博士. 研究方向：卫星总体技术. E-mail： boyish9747@outlook.com
  • 基金资助:
    国家重点研发计划项目(2016YFB0501003);国家自然科学基金项目(61690214);上海市科技创新行动计划人工智能专项项目(19511120900);上海市科技创新行动计划高新技术专项项目(19511106200)

Motion estimation for non-cooperative target based on strong tracking cubature Kalman filter

Shao-biao XIE1,2(),Yu ZHANG3,Kai-rui WEN3,Shuo ZHANG3,Zong-ming LIU3(),Nai-ming QI1   

  1. 1.School of Astronautics，Harbin Institute of Technology，Harbin 150090，China
    2.Shanghai Academy of Spaceflight Technology，Shanghai 201109，China
    3.Shanghai Institute of Spaceflight Control Technology，Shanghai 201109，China
  • Received:2020-05-28 Online:2021-07-01 Published:2021-07-14
  • Contact: Zong-ming LIU E-mail:boyish9747@outlook.com;zongmingliu@163.com

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摘要：

针对飞机、卫星等空天非合作目标运动状态估计时，由于视觉系统检测的特征发生变化会导致系统估计误差增大甚至发散等问题，提出了一种强跟踪容积卡尔曼滤波算法（STCKF）。在利用CW方程描述卫星相对运动时，考虑视觉测量系统的偏心安装，在STCKF预测状态误差协方差阵中引入次优渐消因子，通过在线调整增益，保证残差序列相互正交，使得当系统状态发生突变时仍能保证对系统跟踪的可靠性和稳定性。仿真结果表明，与标准无迹卡尔漫滤波（UKF）和容积卡尔曼滤波（CKF）算法相比，STCKF能够适应目标特征时变的情况，明显改善了目标跟踪的精度和稳定度。

关键词: 航天工程与力学, 强跟踪滤波, 容积卡尔曼滤波, 视觉测量, 非合作目标, 状态估计

Abstract:

In the estimation of the motion state of non-cooperative targets such as aircraft and satellites, due to changes in the features detected by the visual system, the system estimation error will increase or even diverge. This paper proposes a strong tracking volume Kalman filter algorithm (STCKF). When using the CW equation to describe the relative motion of the satellite, considering the eccentric installation of the visual measurement system, a suboptimal fading factor is introduced in the covariance matrix of the STCKF prediction state error, and the gain is adjusted online to ensure that the residual sequences are orthogonal to each other, which guarantee the reliability and stability of the system tracking when the state changes suddenly. The simulation results show that, compared with the standard UKF and CKF algorithms, STCKF can adapt to the time-varying characteristics of the target features, and significantly improve the accuracy and stability of target tracking.

Key words: aerospace engineering and mechanics, strong tracking filter, cubature Kalman filter, vision measurement, non-cooperative target, state estimation

中图分类号: 

  • V19

图1

追踪星与目标星的相对运动"

图2

立体视觉成像示意图"

图3

相对位置误差"

图4

相对速度误差"

图5

相对姿态误差"

图6

相对角速度误差"

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