煤矿井下动态环境下的改进OSELM定位算法  

Improved OSELM localization algorithm in dynamic environment of underground coal mine

作　　者：窦占树 崔丽珍[1] 洪金祥 史明泉[1] DOU Zhanshu;CUI Lizhen;HONG Jinxiang;SHI Mingquan(School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou 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：《测绘通报》2023年第5期90-95,共6页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金(61761038,62261042);内蒙古自然科学基金(2020MS06027)。

摘　　要：在复杂多变的煤矿井下环境及时并准确地获取井下作业人员位置非常重要,井下通信环境高动态变化导致模型定位精度降低。本文将在线顺序极限学习机(OSELM)算法用于井下定位,与批量式定位算法GA-BP和ELM相比,OSELM算法能更有效地维持原模型定位精度。但OSELM算法存在病态矩阵求逆和平等对待所有新增数据的不足,导致该算法的稳定性和对动态环境的适应能力较差。本文在OSELM算法的基础上分别提出正则化OSELM算法、遗忘因子OSELM算法,以及融合正则化技术和遗忘因子机制的OSELM算法。试验表明,试验环境变化后,OSELM算法的定位精度比GA-BP和ELM算法的定位精度分别高1.4282和1.1622 m;在3 m误差距离范围内,所提正则化和遗忘因子的OSELM算法的定位精度均比OSELM算法高,融合两种机制的OSELM算法的定位准确率最高,比OSELM算法高5%左右。OSELM及其改进算法均能有效提高模型定位精度。It is important to obtain the location of underground operators in a timely and accurate manner in the complex and changing underground coal mine environment,where the highly dynamic changes in the underground communication environment lead to a decrease in model localization accuracy.In this paper,the online sequential extreme learning machine(OSELM)algorithm is used for underground localization.Compared with the batch-type localization algorithms GA-BP and extreme learning machine(ELM),the OSELM algorithm can maintain the original model localization accuracy more effectively.However,the OSELM algorithm still suffers from the deficiencies of sickness matrix inversion and equal treatment of all added data,which leads to the poor stability and adaptability of the algorithm to dynamic environments.Based on the OSELM algorithm,the regularization OSELM algorithm and the forgetting factor OSELM algorithm are proposed respectively,while the OSELM algorithm that integrates the regularization technique and the forgetting factor mechanism is proposed.The experiments show that the localization accuracy of the OSELM algorithm is 0.566 and 0.6282 m higher than that of the GA-BP and ELM algorithms,respectively,after the change of the experimental environment;the localization accuracy of the proposed OSELM algorithm with regularization and forgetting factor is higher than that of the OSELM algorithm in the 3 m error distance range;the localization accuracy of the OSELM algorithm that fuses the two mechanisms is the highest.The localization accuracy of the OSELM algorithm with the fusion of the two mechanisms is about 5%higher than that of the OSELM algorithm.Both the proposed OSELM and its improved algorithm can effectively improve the model localization accuracy.

关 键 词：OSELM定位模型 高动态井下环境 正则化技术 遗忘因子机制 增量式学习 位置指纹定位 

分 类 号：P228[天文地球—大地测量学与测量工程]