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地磁测量卫星的矢量磁场在轨标定算法仿真

杜雯 黄河 周军

杜雯, 黄河, 周军. 地磁测量卫星的矢量磁场在轨标定算法仿真[J]. 空间科学学报, 2022, 42(6): 1193-1203. doi: 10.11728/cjss2022.06.211224131
引用本文: 杜雯, 黄河, 周军. 地磁测量卫星的矢量磁场在轨标定算法仿真[J]. 空间科学学报, 2022, 42(6): 1193-1203. doi: 10.11728/cjss2022.06.211224131
DU Wen, HUANG He, ZHOU Jun. Simulation of Vector Magnetic Field In-orbit Calibration Algorithm for Geomagnetic Survey Satellite (in Chinese). Chinese Journal of Space Science, 2022, 42(6): 1193-1203 doi: 10.11728/cjss2022.06.211224131
Citation: DU Wen, HUANG He, ZHOU Jun. Simulation of Vector Magnetic Field In-orbit Calibration Algorithm for Geomagnetic Survey Satellite (in Chinese). Chinese Journal of Space Science, 2022, 42(6): 1193-1203 doi: 10.11728/cjss2022.06.211224131

地磁测量卫星的矢量磁场在轨标定算法仿真

doi: 10.11728/cjss2022.06.211224131
基金项目: 国家自然科学基金面上项目资助(62073261)
详细信息
    作者简介:

    杜雯:E-mail:duwen1998@163.com

    通讯作者:

    黄河,E-mail:huanghe1984@nwpu.edu.cn

  • 中图分类号: P353.1

Simulation of Vector Magnetic Field In-orbit Calibration Algorithm for Geomagnetic Survey Satellite

  • 摘要: 以SWARM为代表的高精度地磁测量卫星对地球磁场探测精度经过标定之后优于0.5 nT,对于开展地磁科学研究具有重要意义。地磁测量卫星通过安装在伸展杆上的矢量磁通门磁强计、标量磁强计和高精度星敏感器,获取测量方向的惯性空间姿态的地磁信息,其中高精度标量磁强计主要用于对磁通门矢量磁强计进行标定。针对地磁测量卫星,研究了矢量磁强计在轨测量误差的校正方法。考虑到矢量磁强计非正交角、标度因子以及偏差的影响,建立磁场矢量线性输出模型;结合标量磁强计的测量值分别设计基于小量近似的线性校正算法和基于参数辨识更新的非线性校正算法;校验两种算法的标定精度,并通过Tukey权重函数改善算法的鲁棒性。仿真结果表明,两种算法校正结果相似,磁场三轴误差可校正至0.5 nT以内,在标量磁强计存在异常值时仍具有较好的校正效果。

     

  • 图  1  非正交角定义

    Figure  1.  Definition of the non-orthogonalities

    图  2  全球等强度地磁分布

    Figure  2.  Global geomagnetic map of equal intensity

    图  3  采样频率为50 Hz时的磁场仿真数据

    Figure  3.  Magnetic field simulation data at sampling frequency of 50 Hz

    图  4  三轴初始测量误差

    Figure  4.  Initial measurement errors of the three axes

    图  5  经线性模型校正后的磁场三轴误差变化曲线以及标量残差

    Figure  5.  Magnetic field errors and scalar residuals after linear model correction

    图  6  经非线性模型校正后的磁场三轴误差以及标量残差变化曲线

    Figure  6.  Magnetic field errors and scalar residuals after nonlinear model correction

    图  7  添加幅值为0.15 nT高斯白噪声干扰后的校正结果

    Figure  7.  Calibrating results disturbed by Gaussian white noise which amplitude is 0.15 nT

    图  8  添加幅值为1 nT高斯白噪声干扰后的校正结果

    Figure  8.  Calibrating results disturbed by Gaussian white noise which amplitude is 1 nT

    图  9  添加幅值为0.5 nT均匀分布噪声和周期性噪声干扰后的校正结果

    Figure  9.  Calibrating results disturbed by uniform noise and periodical noise which amplitude is 0.5 nT

    图  10  标量数据中添加2 nT异常扰动后的校正结果

    Figure  10.  Calibrating results of scalar data adding 2 nT anomaly perturbation

    图  11  标量数据中添加10 nT异常扰动后的校正结果

    Figure  11.  Calibrating results of scalar data adding 10 nT anomaly perturbation

    表  1  不同情况下非线性校正算法参数辨识结果与初始参数对比

    Table  1.   Parameter identification results of the nonlinear correction algorithm in different cases compared with the initial parameters

    仿真条件固有偏差/nT标度因子非正交角/(°)
    SxSySz${U_1}$${U_2}$${U_3}$
    初始参数 [10.00,20.00,30.00]T 1.002500 1.002600 1.002400 0.0100 0.0200 0.0300
    无干扰 [10.28,20.13,30.29]T 1.002499 1.002594 1.002399 0.0101 0.0204 0.0302
    高斯白噪声
    (幅值0.15 nT)
    [11.98,20.91,30.50]T 1.002549 1.002587 1.002404 0.0109 0.0222 0.0304
    高斯白噪声
    (幅值1 nT)
    [15.22,19.82,30.94]T 1.002645 1.002615 1.002416 0.0099 0.0243 0.0271
    均匀分布噪声
    (幅值0.5 nT)
    [11.93,19.77,30.31]T 1.002547 1.002599 1.002400 0.0097 0.0220 0.0286
    周期性噪声
    (幅值0.5 nT)
    [11.50,20.29,30.44]T 1.002535 1.002595 1.002403 0.0103 0.0215 0.0298
    标量数据存在
    2 nT异常扰动
    [10.42,19.72,30.23]T 1.002504 1.002598 1.002398 0.0097 0.0205 0.0295
    标量数据存在10 nT
    异常扰动(应用Tukey
    权重函数后)
    [10.38,20.15,30.30]T 1.002503 1.002594 1.002400 0.0101 0.0204 0.0302
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-23
  • 录用日期:  2022-04-11
  • 修回日期:  2022-06-09
  • 网络出版日期:  2022-11-05

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