采用矩谐分析和支持向量机的地磁导航基准图构建方法

乔玉坤; 王仕成; 张金生; 张琪; 孙渊

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采用矩谐分析和支持向量机的地磁导航基准图构建方法

更新时间：2025-07-09

- 采用矩谐分析和支持向量机的地磁导航基准图构建方法
- A Constructing Method of Reference Maps for Geomagnetic Navigation Using Rectangular Harmonic Analysis and Support Vector Machine
- 西安交通大学学报 2010年44卷第10期页码：47-51
- 作者机构：
  
  第二炮兵工程学院精确制导与仿真实验室,西安,710025
- 作者简介：
- 基金信息：
  
  : 2010-02-21. 作者简介: 乔玉坤(1976-),男,工程师. 基金项目: 国家自然科学基金资助项目(60874093)
- DOI：
  中图分类号： U666.1
- 网络首发：2010-10-10，
  
  纸质出版：2010
- 稿件说明：
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乔玉坤, 王仕成, 张金生, 等. 采用矩谐分析和支持向量机的地磁导航基准图构建方法[J]. 西安交通大学学报, 2010,44(10):47-51.

A Constructing Method of Reference Maps for Geomagnetic Navigation Using Rectangular Harmonic Analysis and Support Vector Machine[J]. 2010, 44(10): 47-51.
乔玉坤, 王仕成, 张金生, 等. 采用矩谐分析和支持向量机的地磁导航基准图构建方法[J]. 西安交通大学学报, 2010,44(10):47-51. DOI：

A Constructing Method of Reference Maps for Geomagnetic Navigation Using Rectangular Harmonic Analysis and Support Vector Machine[J]. 2010, 44(10): 47-51. DOI：

摘要

为便于在地磁导航工程应用中进行载体磁场和地磁场变化的修正

提出了一种采用矩谐分析和支持向量机的地磁基准图构建方法.在导航区域中心采用矩谐分析法来预测基准数据

包括地磁场剩余值计算、地理坐标转换、地磁分量转换、矩谐系数求解及中心区域基准数据预测等

以提高地磁基准图的精度并利于修正; 在导航区域边缘采用支持向量机法来预测基准数据

包括核函数选取、参数优化、模型训练及边缘区域数据预测等

以减弱边界效应的影响; 采用粒子群优化算法对支持向量机参数进行优化

以提高算法的运算效率.试验结果表明

该方法可提高所构建地磁基准图的精度

降低算法的运行时间.

Abstract

A constructing method of geomagnetic reference maps is proposed using rectangular harmonic analysis and support vector machine to conveniently correct both the magnetic field variation of vehicles and geomagnetic field variation in engineering application of geomagnetic navigation. Rectangular harmonic analysis method is adopted to predict reference data in heartland of the navigation region so that the accuracy of geomagnetic reference maps can be improved or conveniently corrected. The processes include calculation of geomagnetic field remnants

transformations of geographical coordinates and geomagnetic components

calculation of rectangular harmonic coefficients

and prediction of reference data in heartland region

etc. The support vector machine method is adopted to predict reference data in the margin of the navigation region to reduce the influence of edge effects

and the data include selection of kernel functions

optimization of parameters

model training

and prediction of reference data in marginal region

etc. Parameters of support vector machine are determined using particle swarm optimization algorithm to improve the computational efficiency of the method. Experimental results show that the accuracy of geomagnetic reference maps constructed by the proposed method is improved and the runtime of the proposed method is reduced markedly.

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references

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