煤矿井下HBi-LSTM地磁定位算法研究  

Research on HBi-LSTM Geomagnetic Localization Algorithm in Underground Coal mines

作　　者：郝婷 崔丽珍[1] 杨勇[1] HAO Ting;CUI Lizhen;YANG Yong(School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou Inner Mongolia 014010,China)

机构地区：[1]内蒙古科技大学信息工程学院,内蒙古包头014010

出　　处：《传感技术学报》2024年第1期98-103,共6页Chinese Journal of Sensors and Actuators

基　　金：内蒙古自然科学基金项目(2020MS06027);内蒙古自治区科技计划项目(2019GG328);内蒙古自治区科技计划项目(2022YFSH0051);国家自然科学基金项目(62261042)。

摘　　要：针对井下环境对地磁数据影响较大的问题,提出HBi-LSTM神经网络地磁定位模型。基于分层LSTM处理不同长短时间序列以及Bi-LSTM充分学习每条序列信息的特点,构建出HBi-LSTM模型,利用矿用手机内置磁力计采集井下地磁数据,建立面向井下环境的地磁指纹数据库,通过HBi-LSTM学习实现地磁序列可以更好地对应位置标签,之后矿工手持矿用手机随机运动采集地磁序列通过训练好的模型精确匹配指纹库实现在线定位。实验结果显示:所提出的模型比基本LSTM模型的定位性能更好,能够有效提升复杂环境下定位精度。In response to the problem that the downhole environment has a large influence on the geomagnetic data,an HBi-LSTM neural network geomagnetic localization model is proposed.Based on the characteristics of hierarchical LSTM processing different length time sequences and Bi-LSTM fully learning the information of each sequence,the HBi-LSTM model is constructed to collect underground geo-magnetic data by using the built-in magnetometer of mining cell phone to establish a geomagnetic fingerprint database for underground environment,and through HBi-LSTM learning to achieve geomagnetic sequences better corresponding to location tags,after which miners holding mining cell phones collect geomagnetic sequences in random motion to achieve online localization by precisely matching the trained model to the fingerprint database.The experimental results show that the proposed model has better localization performance than the basic LSTM model and can effectively improve the localization accuracy in complex environments.

关 键 词：地磁定位 指纹数据库 HBi-LSTM定位模型 煤矿井下 

分 类 号：TD76[矿业工程—矿井通风与安全] TN92[电子电信—通信与信息系统]