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

doi:10.11959/j.issn.1000-0801.2021201

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

Research and Development | 更新时间：2024-06-05

- Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network
- Telecommunications Science Vol. 37, Issue 8, Pages: 85-95(2021)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2021201
  CLC： TN913
- Published Online：2021-08，
  
  Published：20 August 2021
- 稿件说明：
移动端阅览
Zhichao ZHOU, Yi FENG, Xiaohan XIA, et al. Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network[J]. Telecommunications science, 2021, 37(8): 85-95.
DOI：

Zhichao ZHOU, Yi FENG, Xiaohan XIA, et al. Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network[J]. Telecommunications science, 2021, 37(8): 85-95. DOI： 10.11959/j.issn.1000-0801.2021201.

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。

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.

关键词

Keywords

references

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