空间科学学报2025，Vol.45Issue(5)：1211-1219,9.DOI:10.11728/cjss2025.05.2024-0109
基于极限学习机的地磁模型误差预测方法
Error Prediction Method of Geomagnetic Model Based on Extreme Learning Machine
摘要
Abstract
The high-precision geomagnetic field model is an important foundation for autonomous na-vigation of near earth satellites,but the improvement of navigation accuracy is constrained by observa-tion errors,spherical harmonic truncation errors,and slow updates of the geomagnetic model.To solve this problem,this paper proposes a geomagnetic model error prediction method based on regularized ex-treme learning machine.The optimal estimation of the regularization coefficient C is achieved by using a subtraction mean algorithm,which reduces subjectivity and randomness in parameter tuning,improves learning efficiency and prediction accuracy.In addition,this method can effectively improve the error es-timation accuracy when outliers exist in geomagnetic observation sequences.Then,a geomagnetic navi-gation method with model error compensation was proposed by integrating it with filtering algorithms,and simulation verification was conducted using real geomagnetic measurement data from in orbit satel-lites.The results show that the prediction accuracy of the method proposed in this paper is superior to several commonly used neural network prediction methods,and the navigation accuracy reaches 1.26 km,indicating that the proposed error prediction model can effectively improve the performance of geomag-netic navigation.关键词
极限学习机/正则化/减法均值器/地磁滤波Key words
Extreme learning machine/Regularization/Subtraction mean algorithm/Geomagnetic filter分类
地球科学引用本文复制引用
郭红阳,张涛,韩鹏,陈晨,赵治华..基于极限学习机的地磁模型误差预测方法[J].空间科学学报,2025,45(5):1211-1219,9.基金项目
河南省科技厅自然科学项目资助(242102221043) （242102221043）
