航空学报 > 2023, Vol. 44 Issue (3): 526120-526120   doi: 10.7527/S1000-6893.2021.26120

考虑有色噪声影响的脉冲星导航2级强跟踪差分滤波器

许强(), 崔洪亮, 丁邦平, 赵爱罡, 赵阳   

  1. 青州高新技术研究所 测试控制系,潍坊 262500
  • 收稿日期:2021-07-15 修回日期:2021-07-29 接受日期:2021-08-05 出版日期:2023-02-15 发布日期:2021-08-25
  • 通讯作者: 许强 E-mail:xuq1993@foxmail.com

Two-stage strong tracking differential Kalman filter for X-ray pulsar navigation with coloured noise

Qiang XU(), Hongliang CUI, Bangping DING, Aigang ZHAO, Yang ZHAO   

  1. Department of Test and Control,Qingzhou Research Institute of High-technology,Weifang 262500,China
  • Received:2021-07-15 Revised:2021-07-29 Accepted:2021-08-05 Online:2023-02-15 Published:2021-08-25
  • Contact: Qiang XU E-mail:xuq1993@foxmail.com

摘要:

为提高X射线脉冲星导航对有色噪声及太阳系内星历误差的鲁棒性,设计了2级强跟踪差分滤波器(TSTDKF)。首先在分析导航原理基础上,导出了中心天体星历误差对导航结果的误差传递关系,并利用扩展卡尔曼滤波器(EKF)进行了仿真验证;在同一运行轨道上,又结合引力摄动模型对第三天体引力摄动数据进行了分析,证明该部分噪声为有色噪声。根据以上结论,将普通2级卡尔曼滤波器(TKF)的无偏滤波器设计为一种改进的差分卡尔曼滤波器以降低有色噪声对导航系统的影响,同时又在其独立偏差滤波器中根据观测残差构建了多重自适应调节因子以增强其跟踪性能,两者共同构成TSTDKF的2个并行滤波器。通过仿真实验证明,TSTDKF的位置误差性能最大可比EKF和TKF改进56.49%和35.18%,速度误差性能改进27.66%和17.07%;对星历误差的跟踪效果也整体好于TKF。

关键词: 星历误差, 有色噪声, 2级卡尔曼滤波器, 差分卡尔曼滤波器, 强跟踪卡尔曼滤波器

Abstract:

To improve the robustness of X-ray pulsar navigation to colored noise and ephemeris errors, a Two-stage Strong Tracking Differential Kalman Filter (TSTDKF) is designed. First, based on an analysis of the navigation principle, the error transfer relationship of the ephemeris error of central celestial body to the navigation result is analyzed, and the Extended Kalman Filter (EKF) is used for simulation verification. On the same orbit, the gravitational perturbation data of the third celestial body is analyzed using the gravitational perturbation model, which proves that the noise is colored noise. Based on the above conclusions, a bias-free filter of ordinary Two-stage Kalman Filter (TKF) is designed as an improved differential filter to reduce the impact of colored noise on the navigation system. In TSTDKF’s separate bias filter, multiple adaptive adjustment factors are constructed based on observation residuals to enhance its tracking performance. The two filters together form the two parallel filters of TSTDKF. Simulation experiments prove that the positioning performance of TSTDKF is 56.49% and 35.18% better than that of EKF and TKF, respectively, and the velocity of TSTDKF is also 27.66% and 17.07% better than that of EKF and TKF, respectively. Its tracking accuracy of ephemeris error is also overall better than that of TKF.

Key words: Ephemeris error, colored noise, two-stage Kalman filter, differential Kalman filter, strong tracking Kalman filter

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