RIM-Net: A Real-Imaginary-Magnitude Network for NLOS/LOS Identification in UWB Indoor Positioning Systems
In UWB-based indoor positioning research, NLOS signals severely degrade localization accuracy. To address the NLOS/LOS discrimination challenge, this paper proposes the RIM-Net model, which enhances CIR signal feature learning by integrating residual blocks and LSTM architectures. Unlike conventiona...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11098758/ |
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| author | Jiacheng Ni Fang Li Shuai Cao Linsong Li |
| author_facet | Jiacheng Ni Fang Li Shuai Cao Linsong Li |
| author_sort | Jiacheng Ni |
| collection | DOAJ |
| description | In UWB-based indoor positioning research, NLOS signals severely degrade localization accuracy. To address the NLOS/LOS discrimination challenge, this paper proposes the RIM-Net model, which enhances CIR signal feature learning by integrating residual blocks and LSTM architectures. Unlike conventional methods that process only the magnitude of CIR signals, RIM-Net explicitly models both real and imaginary components of CIR signals to improve classification accuracy. Experimental results demonstrate that RIM-Net achieves an average accuracy of 92.36% on benchmark datasets while maintaining generalization capabilities above 85% in unseen environments. Furthermore, the optimization of the TOA algorithm validated the effectiveness of RIM-Net in localization, achieving an average performance improvement of 9.66%. This approach provides an effective solution for NLOS/LOS identification in complex channel conditions within UWB positioning systems. |
| format | Article |
| id | doaj-art-ae15c2a29b1045e9901c76164abb72c0 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ae15c2a29b1045e9901c76164abb72c02025-08-20T03:22:19ZengIEEEIEEE Access2169-35362025-01-011313459013460010.1109/ACCESS.2025.359357811098758RIM-Net: A Real-Imaginary-Magnitude Network for NLOS/LOS Identification in UWB Indoor Positioning SystemsJiacheng Ni0Fang Li1https://orcid.org/0009-0004-9926-2218Shuai Cao2https://orcid.org/0009-0009-3817-2872Linsong Li3School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, ChinaIn UWB-based indoor positioning research, NLOS signals severely degrade localization accuracy. To address the NLOS/LOS discrimination challenge, this paper proposes the RIM-Net model, which enhances CIR signal feature learning by integrating residual blocks and LSTM architectures. Unlike conventional methods that process only the magnitude of CIR signals, RIM-Net explicitly models both real and imaginary components of CIR signals to improve classification accuracy. Experimental results demonstrate that RIM-Net achieves an average accuracy of 92.36% on benchmark datasets while maintaining generalization capabilities above 85% in unseen environments. Furthermore, the optimization of the TOA algorithm validated the effectiveness of RIM-Net in localization, achieving an average performance improvement of 9.66%. This approach provides an effective solution for NLOS/LOS identification in complex channel conditions within UWB positioning systems.https://ieeexplore.ieee.org/document/11098758/Deep learningUWBCIRsLSTM |
| spellingShingle | Jiacheng Ni Fang Li Shuai Cao Linsong Li RIM-Net: A Real-Imaginary-Magnitude Network for NLOS/LOS Identification in UWB Indoor Positioning Systems IEEE Access Deep learning UWB CIRs LSTM |
| title | RIM-Net: A Real-Imaginary-Magnitude Network for NLOS/LOS Identification in UWB Indoor Positioning Systems |
| title_full | RIM-Net: A Real-Imaginary-Magnitude Network for NLOS/LOS Identification in UWB Indoor Positioning Systems |
| title_fullStr | RIM-Net: A Real-Imaginary-Magnitude Network for NLOS/LOS Identification in UWB Indoor Positioning Systems |
| title_full_unstemmed | RIM-Net: A Real-Imaginary-Magnitude Network for NLOS/LOS Identification in UWB Indoor Positioning Systems |
| title_short | RIM-Net: A Real-Imaginary-Magnitude Network for NLOS/LOS Identification in UWB Indoor Positioning Systems |
| title_sort | rim net a real imaginary magnitude network for nlos los identification in uwb indoor positioning systems |
| topic | Deep learning UWB CIRs LSTM |
| url | https://ieeexplore.ieee.org/document/11098758/ |
| work_keys_str_mv | AT jiachengni rimnetarealimaginarymagnitudenetworkfornloslosidentificationinuwbindoorpositioningsystems AT fangli rimnetarealimaginarymagnitudenetworkfornloslosidentificationinuwbindoorpositioningsystems AT shuaicao rimnetarealimaginarymagnitudenetworkfornloslosidentificationinuwbindoorpositioningsystems AT linsongli rimnetarealimaginarymagnitudenetworkfornloslosidentificationinuwbindoorpositioningsystems |