电信科学 ›› 2021, Vol. 37 ›› Issue (8): 85-95.doi: 10.11959/j.issn.1000-0801.2021201

• 研究与开发 • 上一篇    

基于移动蜂窝网的机器学习室外指纹定位方案

周志超, 冯毅, 夏小涵, 冯瑜瑶, 蔡超, 邱佳慧, 杨立辉, 乌云霄   

  1. 中国联合网络通信有限公司智网创新中心,北京 100048
  • 修回日期:2021-08-10 出版日期:2021-08-01 发布日期:2021-08-01
  • 作者简介:周志超(1989− ),男,中国联合网络通信集团有限公司智网创新中心工程师,主要研究方向为高精度定位、蜂窝移动通信、C-V2X等
    冯毅(1976− ),男,中国联合网络通信集团有限公司智网创新中心高级工程师,主要研究方向为移动通信系统网络规划、认知无线电、异构无线网络传输技术、5G垂直应用等
    夏小涵(1989− ),男,中国联合网络通信集团有限公司智网创新中心 C-V2X 研发工程师,主要研究方向为高精度定位、RSU、车路协同等
    冯瑜瑶(1995− ),女,中国联合网络通信集团有限公司智网创新中心通信工程师,主要研究方向为无线移动通信、高精度定位、C-V2X及MEC解决方案等
    蔡超(1984− ),男,中国联合网络通信集团有限公司智网创新中心总监,主要研究方向为无线移动通信、高精度定位、数据网、安全网关等
    邱佳慧(1985− ),女,中国联合网络通信集团有限公司智网创新中心车联网技术总监,主要研究方向为车联网、5G通信、高精度定位等
    杨立辉(1986− ),男,中国联合网络通信集团有限公司智网创新中心解决方案经理,主要研究方向为无线移动通信、高精度定位、视频编解码及传输等
    乌云霄(1985− ),女,中国联合网络通信集团有限公司智网创新中心高级工程师,主要研究方向为移动通信、移动网络OSS

Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network

Zhichao ZHOU, Yi FENG, Xiaohan XIA, Yuyao FENG, Chao CAI, Jiahui QIU, Lihui YANG, Yunxiao WU   

  1. Center of Smart Network of China United Network Communication Co., Ltd., Beijing 100048, China
  • Revised:2021-08-10 Online:2021-08-01 Published:2021-08-01

摘要:

基于移动蜂窝网络技术的定位方案是提供网络优化、紧急救援、公安巡警和位置服务等应用的重要技术途径之一。传统的基于小区基站位置信息的定位方案定位精度低、定位误差大,无法满足某些定位应用需求。基于指纹定位的方案能够在基于小区粗定位方案基础上大幅度提升定位精度、节约计算成本、增强适用性,成为定位研究的热点。针对室外指纹定位的业务需求,深入研究分析了两种基于机器学习的栅格化和非栅格化室外指纹定位方案。通过参数加权、数据拟合等方法对于大规模指纹数据进行了清洗,提高数据源的有效性。通过划定研究区域、栅格化、构建指纹数据库、训练模型、修正模型、非栅格化、粗定位耦合、匹配参数、训练参数等子模块的实现,分析和优化了算法的运行效率和定位精度,确定了影响算法性能的关键指标。进而结合仿真结果,分析了两种基于指纹的定位方案的性能。最后介绍了基于机器学习的指纹定位方案在实际应用中的典型场景。

关键词: 指纹定位, 移动蜂窝网络, 机器学习, 栅格化, 典型应用场景

Abstract:

The positioning scheme based on mobile cellular network technology is one of the important technical approaches to provide network optimization, emergency rescue, police patrol and location services.The traditional positioning scheme based on cell base station location information has low positioning accuracy and large positioning error, so it cannot meet the requirements of some positioning applications.The scheme based on fingerprint location can greatly improve the location accuracy, save computational cost and enhance the usability based on the coarse location scheme of the cell and become the hotspot of the research.Rasterization and non-rasterization of outdoor fingerprint location scheme based on machine learning were studied and analyzed to meet the business requirements of outdoor fingerprint location.By means of parameter weighting, data fitting and other methods, large-scale fingerprint data were cleaned to improve the effectiveness of data sources.Through the realization of sub-modules such as demarcating research area, rasterizing, constructing fingerprint database, training model, correcting model, non-rasterizing, rough positioning coupling, matching parameter and training parameter, the operation efficiency and positioning accuracy of the algorithm were analyzed and optimized, and the key indexes affecting the algorithm performance were determined.Then, the performance of two fingerprint-based localization schemewas analyzed based on the simulation results.Finally, the typical scenarios of the fingerprint location scheme based on machine learning in practical application were presented.

Key words: fingerprint positioning, mobile cellular network, machine learning, grid, typical application scenario

中图分类号: 

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