摘要
磁性物体在磁场中产生的扰动信号为目标定位与跟踪提供了非接触式、无源的技术手段。传统基于物理建模的磁矢量定位方法在复杂环境中面临精度下降和鲁棒性不足的挑战。本文提出了一种磁异常跟踪卷积神经网络(MagTrack-CNN),旨在解决磁性物体的高精度空间动态定位。该方法采用双层磁矢量数据观测架构,通过垂直梯度信息增强深度方向的定位敏感性,设计了双分支独立预测网络结构,避免各维度间的梯度竞争;构建了多尺度特征提取框架,有效捕获不同空间尺度的磁异常模式。稳定磁场区域内、单个均匀磁化目标定位场景下的实验结果表明,MagTrack-CNN在测试集上实现了1.98 cm的整体定位精度以及良好的z轴定位效果。实验针对不同运动轨迹类型的测试均实现了有效的定位跟踪。同时,噪声敏感性实验表明,在0.5%~3.0%的测量噪声水平下,模型仍能保持较低误差(平均绝对误差由0.42 cm增至0.46 cm)。此外,该方法的单样本推理时间保持在2.3 ms以内,展示了良好的应用前景。
The disturbance signals generated by ferromagnetic objects in a magnetic field provide a non-contact and passive means for target localization and tracking.However,traditional magnetic vector localization methods based on physical modeling suffer from degraded accuracy and limited robustness in complex environments.In this paper,we propose a magnetic anomaly tracking convolutional neural network(MagTrack-CNN)for high-precision spatial dynamic localization of ferromagnetic objects.The proposed method employs a dual-layer magnetic vector observation architecture to enhance localization sensitivity along the depth direction by incorporating vertical gradient information.A dual-branch independent prediction network is designed to mitigate gradient competition among spatial components,while a multi-scale feature extraction framework is constructed to effectively capture magnetic anomaly patterns at different spatial scales.Experimental results in stable magnetic field regions for localization scenarios involving a single uniformly magnetized target demonstrate that MagTrack-CNN achieves an overall positioning accuracy of 1.98 cm on the test set,and exhibits superior accuracy in z-axis localization.In dynamic localization tests across different trajectory types,the method consistently produces correct dynamic positioning.Meanwhile,noise sensitivity experiments show that under measurement noise levels of 0.5%-3.0%,the model maintains low errors(the mean absolute error increases from 0.42 cm to 0.46 cm).Furthermore,the per-sample inference time remains within 2.3 ms,highlighting its promising potential for practical applications.
作者
高全明
尚复庆
柴进
王一
孙伟
赵静
Gao Quanming;Shang Fuqing;Chai Jin;Wang Yi;Sun Wei;Zhao Jing(College of Instrumentation&Electrical Engineering,Jilin University,Changchun 130061,China;College of Electronics and Information,Qingdao University,Qingdao 266071,Shandong,China;Central China Branch of National Petroleum and Natural Gas Pipeline Network Group Co.,Ltd.,Wuhan 430000,China)
出处
《吉林大学学报(地球科学版)》
北大核心
2025年第6期2088-2099,共12页
Journal of Jilin University:Earth Science Edition
基金
吉林省教育厅青年项目(JJKH20241274KJ)
山东省科技厅青年基金项目(ZR2024QD155)
国家石油天然气管网集团有限公司科研项目(GWHT20230038719)。
关键词
磁目标定位
卷积神经网络
多尺度特征
轨迹跟踪
magnetic target localization
convolutional neural network
multi-scale feature
trajectory tracking
