Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network
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 positionin...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2021-08-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021201/ |
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| author | Zhichao ZHOU Yi FENG Xiaohan XIA Yuyao FENG Chao CAI Jiahui QIU Lihui YANG Yunxiao WU |
| author_facet | Zhichao ZHOU Yi FENG Xiaohan XIA Yuyao FENG Chao CAI Jiahui QIU Lihui YANG Yunxiao WU |
| author_sort | Zhichao ZHOU |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-54fcfb894845417e927c5163022cd4d9 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2021-08-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-54fcfb894845417e927c5163022cd4d92025-01-15T03:32:34ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012021-08-0137859559814565Outdoor location scheme with fingerprinting based on machine learning of mobile cellular networkZhichao ZHOUYi FENGXiaohan XIAYuyao FENGChao CAIJiahui QIULihui YANGYunxiao WUThe 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021201/fingerprint positioningmobile cellular networkmachine learninggridtypical application scenario |
| spellingShingle | 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 Dianxin kexue fingerprint positioning mobile cellular network machine learning grid typical application scenario |
| title | Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network |
| title_full | Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network |
| title_fullStr | Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network |
| title_full_unstemmed | Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network |
| title_short | Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network |
| title_sort | outdoor location scheme with fingerprinting based on machine learning of mobile cellular network |
| topic | fingerprint positioning mobile cellular network machine learning grid typical application scenario |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021201/ |
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