| 王红尧,郑鸿林,田 劼,彭志远,唐文锦.面向矿井动目标的 PSO-SVR 模型与 UWB Chan
优化距离指纹融合定位方法[J].电子测量与仪器学报,2022,36(7):106-114 |
| 面向矿井动目标的 PSO-SVR 模型与 UWB Chan
优化距离指纹融合定位方法 |
| Fusion location method of PSO-SVR model and UWB Chanoptimal fingerprint matching for mine moving target |
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| DOI： |
| 中文关键词: 矿井动目标 双边双向测距 PSO-SVR 模型 指纹定位 |
| 英文关键词:mine moving target bilateral two-way ranging PSO-SVR model fingerprint location |
| 基金项目:中央高校基本科研业务费项目（2021YQJD02）、南昌航空大学重点科研基地开放基金(EW202180222)、北京市优秀人才项目(2015000020124G120)资助 |
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| Author | Institution |
| Wang Hongyao | 1. School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing),2. Key Laboratory of Nondestructive Testing (Nanchang Hangkong University), Ministry of Education,3. Key Laboratory of Coal Mine Intelligence and Robot Innovative Application,Ministry of Emergency Management, China University of Mining and Technology (Beijing) |
| Zheng Honglin | 1. School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing),3. Key Laboratory of Coal Mine Intelligence and Robot Innovative Application,Ministry of Emergency Management, China University of Mining and Technology (Beijing) |
| Tian Jie | 1. School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing),3. Key Laboratory of Coal Mine Intelligence and Robot Innovative Application,Ministry of Emergency Management, China University of Mining and Technology (Beijing) |
| Peng Zhiyuan | 1. School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing),3. Key Laboratory of Coal Mine Intelligence and Robot Innovative Application,Ministry of Emergency Management, China University of Mining and Technology (Beijing) |
| Tang Wenjin | 1. School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing),3. Key Laboratory of Coal Mine Intelligence and Robot Innovative Application,Ministry of Emergency Management, China University of Mining and Technology (Beijing) |
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| 中文摘要: |
| 针对目前井下人员、车辆、设备等移动目标位置精确管理存在的不足,本文对面向矿井动目标的定位算法与指纹定位模
型进行研究。 设计出一种基于改进粒子群优化 SVR 模型与 Chan 优化距离指纹匹配融合定位方法。 首先,构建一种基于
STM32 ARM 主控制器和 DWM1000 的超宽带(UWB)核心节点模型,通过双边双向测距和飞行时间法(TOF)对传输距离数据进
行计算。 在此基础上,通过依次在特定点采集距离指纹,基于改进的 PSO-SVR 模型进行移动目标路线拟合,预测目标的移动路
径。 再将其与 Chan 指纹进行结合,拓展出优化距离指纹融合定位方法。 实验结果表明,本文提出的指纹优化匹配融合定位方
法能够较好地预测出移动路径,最大误差不超过 20 cm,平均误差不超过 1 cm。 本文研究对矿井智能化建设及安全生产具有重
要意义。 |
| 英文摘要: |
| Aiming at improving the deficiency of positioning accuracy of moving targets such as underground personnel, vehicles and
equipment, this paper studies the location algorithm and fingerprint location model of mine moving target and a fusion location method
based on SVR model optimized by improved particle swarm optimization and Chan distance fingerprint is proposed. Firstly, an ultra
wideband (UWB) core node model based on STM32 ARM main controller and DWM1000 is designed, and the transmission distance
data are analyzed through bilateral bidirectional ranging and time of flight ( TOF). On this basis, the moving path of the target is
predicted by successively collecting distance fingerprints at specific points and the moving target route fitting within the improved PSOSVR model. Then it is combined with the Chan algorithm fingerprint, and expand the optimized distance fingerprint fusion location
method. The experimental results show that the optimized distance fingerprint fusion location method can correctly predict the moving
path, with the maximum error of no more than 20 cm and the average error of no more than 1 cm. The study is of great significance to
mine intelligent construction and safety production. |
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