基于LS和ABC-SVM的煤矿井下定位算法研究  

Research on Localization Algorithm in Underground Coal Mine Based on LS and ABC-SVM

作　　者：周伸伸 张梅[1] 崔鹏鹏[1] Zhou Shenshen;Zhang Mei;Cui Pengpeng(School of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《煤矿机械》2025年第5期191-194,共4页Coal Mine Machinery

基　　金：国家自然科学基金项目(52374154)。

摘　　要：为了提高煤矿井下作业的安全性,对井下机车和人员进行精确定位至关重要。研究了一种基于超宽带(UWB)技术的定位算法,旨在解决井下复杂环境中的非视距(NLOS)干扰问题。该算法首先利用最小二乘(LS)法进行初步定位,接着利用人工蜂群(ABC)算法对支持向量机(SVM)进行优化,得到ABC-SVM模型,最后利用ABC-SVM模型对初步定位后的结果进行误差修正。实验结果表明,与LS方法相比,ABC-SVM模型在井下定位的平均欧氏距离误差减少了9.0601 cm。To enhance the safety of underground operations in coal mine,precise localization of underground locomotives and personnel is crucial.Researched on a localization algorithm based on ultra-wideband(UWB)technology,aimed at addressing the non-line-of-sight(NLOS)interference issues in the complex underground environment.This algorithm begins with an initial localization using the least square(LS)method,and subsequently employs the artificial bee colony(ABC)algorithm to optimize a support vector machine(SVM)to obtain an ABC-SVM model.Finally,the ABC-SVM model is utilized to correct the errors of the initial localization results.Experimental results show that compared to the LS method,the ABC-SVM model reduces the average Euclidean distance error in underground localization by 9.0601 cm.

关 键 词：煤矿井下 UWB定位 LS ABC SVM 

分 类 号：TD76[矿业工程—矿井通风与安全]