A UWB NLOS identification method under pedestrian occlusion
Ultrawideband (UWB) is a hot technology for indoor positioning with large bandwidth, strong anti-interference ability, and high multipath resolution capacity.However, due to the complex indoor environment, UWB signal propagation will inevitably be blocked, resulting in non-line-of-sight (NLOS) propa...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2023-12-01
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| Series: | 物联网学报 |
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| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00348/ |
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| _version_ | 1841531124941062144 |
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| author | Tong WU Yeshen LI Zhenhuang HUANG Yu ZHANG Wanle ZHANG Ke XIONG |
| author_facet | Tong WU Yeshen LI Zhenhuang HUANG Yu ZHANG Wanle ZHANG Ke XIONG |
| author_sort | Tong WU |
| collection | DOAJ |
| description | Ultrawideband (UWB) is a hot technology for indoor positioning with large bandwidth, strong anti-interference ability, and high multipath resolution capacity.However, due to the complex indoor environment, UWB signal propagation will inevitably be blocked, resulting in non-line-of-sight (NLOS) propagation, which greatly reduces the accuracy of UWB positioning.Therefore, identifying NLOS signals accurately and discarding or correcting them are important to alleviate the problem of the decline in positioning accuracy.The majority of present NLOS identification work focuses on scenes with building structures such as walls.Further discussion is needed for scenes obscured by pedestrians.Since the impact of human obstacles on the signals is more complex and cannot be ignored, the NLOS identification under pedestrian occlusion was studied.By comparing a variety of machine learning methods and signal feature combinations, the random forest method based on the three-dimensional features of the first path signal power, the received signal power, and the measured distance was proposed.These features with fewer dimensions and easy extraction were used to achieve a high identification percentage for NLOS.The experimental results based on the measured data of different devices show that the NLOS identification accuracy based on the proposed method reaches 99.05%, 99.32% and 98.81% respectively. |
| format | Article |
| id | doaj-art-f766023bb2004ce3ab976bbb76313533 |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2023-12-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-f766023bb2004ce3ab976bbb763135332025-01-15T02:54:18ZzhoChina InfoCom Media Group物联网学报2096-37502023-12-017637159565895A UWB NLOS identification method under pedestrian occlusionTong WUYeshen LIZhenhuang HUANGYu ZHANGWanle ZHANGKe XIONGUltrawideband (UWB) is a hot technology for indoor positioning with large bandwidth, strong anti-interference ability, and high multipath resolution capacity.However, due to the complex indoor environment, UWB signal propagation will inevitably be blocked, resulting in non-line-of-sight (NLOS) propagation, which greatly reduces the accuracy of UWB positioning.Therefore, identifying NLOS signals accurately and discarding or correcting them are important to alleviate the problem of the decline in positioning accuracy.The majority of present NLOS identification work focuses on scenes with building structures such as walls.Further discussion is needed for scenes obscured by pedestrians.Since the impact of human obstacles on the signals is more complex and cannot be ignored, the NLOS identification under pedestrian occlusion was studied.By comparing a variety of machine learning methods and signal feature combinations, the random forest method based on the three-dimensional features of the first path signal power, the received signal power, and the measured distance was proposed.These features with fewer dimensions and easy extraction were used to achieve a high identification percentage for NLOS.The experimental results based on the measured data of different devices show that the NLOS identification accuracy based on the proposed method reaches 99.05%, 99.32% and 98.81% respectively.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00348/UWBindoor positioningNLOS identificationrandom forest |
| spellingShingle | Tong WU Yeshen LI Zhenhuang HUANG Yu ZHANG Wanle ZHANG Ke XIONG A UWB NLOS identification method under pedestrian occlusion 物联网学报 UWB indoor positioning NLOS identification random forest |
| title | A UWB NLOS identification method under pedestrian occlusion |
| title_full | A UWB NLOS identification method under pedestrian occlusion |
| title_fullStr | A UWB NLOS identification method under pedestrian occlusion |
| title_full_unstemmed | A UWB NLOS identification method under pedestrian occlusion |
| title_short | A UWB NLOS identification method under pedestrian occlusion |
| title_sort | uwb nlos identification method under pedestrian occlusion |
| topic | UWB indoor positioning NLOS identification random forest |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00348/ |
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