• 论文 •

一种基于卷积神经网络的地磁基准图构建方法

1. 火箭军工程大学 导弹工程学院, 西安 710025
• 收稿日期:2020-06-16 发布日期:2021-10-09
• 通讯作者: 张金生 E-mail:15309217656@163.com
• 基金资助:
国家自然科学基金（61673017）；中国博士后科学基金（2019M3643）

A geomagnetic reference map construction method based on convolutional neural network

MA Xiaoyu, ZHANG Jinsheng, LI Ting

1. Missile Engineering College, Rocket Force University, Xi'an 710025, China
• Received:2020-06-16 Published:2021-10-09
• Supported by:
National Natural Science Foundation of China (61673017); China Postdoctoral Science Foundation (2019M3643)

Abstract: Geomagnetic matching navigation technology is an important auxiliary navigation guidance method. The construction accuracy of geomagnetic reference map plays a decisive role in the accuracy of geomagnetic matching guidance. Aimed at the problem that the construction accuracy of the existing geomagnetic reference maps is difficult to meet the actual requirements of geomagnetic matching navigation, a construction method of geomagnetic reference maps based on convolutional neural network is proposed. First, the convolutional layer is used to extract the feature image patches in the low-resolution reference image. Then, the Learned Iterative Shrinkage and Thresholding Algorithm (LISTA) is used to realize the sparse representation of the low-resolution image patches. Finally, the three-channel geomagnetic information is used to obtain the final reconstructed high-resolution reference map. The experimental results show that the proposed method has a higher construction accuracy for geomagnetic reference map and better robustness to noise. Various objective evaluation indexes of the proposed method are higher than those of the existing super-resolution reconstruction algorithms.