基于移动蜂窝网的机器学习室外指纹定位方案

doi:10.11959/j.issn.1000-0801.2021201

Abstract

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

CLC Number:

TN913

Zhichao ZHOU, Yi FENG, Xiaohan XIA, Yuyao FENG, Chao CAI, Jiahui QIU, Lihui YANG, Yunxiao WU. Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network[J]. Telecommunications Science, 2021, 37(8): 85-95.

Figures/Tables 15

References 11

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应用分类	应用名称	市场容量	定位精度要求	时间要求
重点行业	三打一查	＞3.5亿次	30～50 m	＜30 s
	医院急救	＞2亿次	5～10 m	＜60 s
	紧急救援	2020年规模超过3 800万元	5～10 m	＜2 min
其他行业	物流运输	全国规模超3 000万元	2 cm	＜1 min
	老人儿童关爱定位	保守估计约1 050万元	5～10 m	＜2 min

指纹定位方案	计算复杂度	性能	鲁棒性	实施性
方案一	较高	有折损	较好	对服务器集群要求较高
方案二	较低	无折损	稍差	对服务器集群要求较低

仿真参数名称	参数值
划定区域Y	1.5 km
统计小区个数	5个
栅格尺寸X	50 m
终端源数据量	1 048 574条
训练集占比	80%
测试集占比	20%
机器学习框架	sklearn.KNeighborsRegressor、sklearn.KNeighborsClassifier
学习和预测规则	KNN、WKNN
终端接收的RSRP个数	3个
经度、纬度精度	1.0×10^-12°
相对距离精度	1m
方案一	基于栅格划分的指纹定位算法
方案二	基于非栅格的指纹定位算法

指纹定位方案	定位准确性	性能折损性	鲁棒性	适合场景
方案一	57%@50 m	50 m	较好	终端密度小的场景
方案二	46%@50 m	0m	稍差	终端密度大的场景

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

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