Attention based LSTM framework for robust UWB and INS integration in NLOS environments
Abstract This paper proposes a novel UWB/INS integration framework that utilizes attention-based Long Short-Term Memory (LSTM) neural networks to address challenges related to UWB signal degradation during non-line-of-sight (NLOS) propagation. The network is adopted to generate pseudo measurements t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-05501-3 |
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| author | Meilin Ren Junyu Wei Jiangyi Qin Xiaojun Guo Haowen Wang Shiqi Li |
| author_facet | Meilin Ren Junyu Wei Jiangyi Qin Xiaojun Guo Haowen Wang Shiqi Li |
| author_sort | Meilin Ren |
| collection | DOAJ |
| description | Abstract This paper proposes a novel UWB/INS integration framework that utilizes attention-based Long Short-Term Memory (LSTM) neural networks to address challenges related to UWB signal degradation during non-line-of-sight (NLOS) propagation. The network is adopted to generate pseudo measurements to maintain Kalman filter measurement update during NLOS. LSTM networks are well-suited for modeling sequential data due to their ability to capture long-term dependencies, making them particularly effective in handling the temporal aspects of navigation data. By leveraging attention mechanisms, the proposed approach enhances temporal feature extraction and improves the accuracy of pseudo-UWB observations generation. Extensive experiments demonstrate that the attention-LSTM model significantly reduces positioning errors under both loosely and tightly coupled configurations in NLOS scenarios. This hybrid fusion of model-based and learning-based techniques ensures robust and precise UWB/INS localization. |
| format | Article |
| id | doaj-art-dcb3bb6f97154e96a109c9768b9d94f8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-dcb3bb6f97154e96a109c9768b9d94f82025-08-20T04:01:36ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-05501-3Attention based LSTM framework for robust UWB and INS integration in NLOS environmentsMeilin Ren0Junyu Wei1Jiangyi Qin2Xiaojun Guo3Haowen Wang4Shiqi Li5CATARC Automotive Test Center (Tianjin) Co., Ltd.College of Intelligence Science and Technoloy, National University of Defense TechnologyNational Innovation Institute of Defense Technology, Academy of Military ScienceCollege of Intelligence Science and Technoloy, National University of Defense TechnologyCollege of Intelligence Science and Technoloy, National University of Defense TechnologyCollege of Intelligence Science and Technoloy, National University of Defense TechnologyAbstract This paper proposes a novel UWB/INS integration framework that utilizes attention-based Long Short-Term Memory (LSTM) neural networks to address challenges related to UWB signal degradation during non-line-of-sight (NLOS) propagation. The network is adopted to generate pseudo measurements to maintain Kalman filter measurement update during NLOS. LSTM networks are well-suited for modeling sequential data due to their ability to capture long-term dependencies, making them particularly effective in handling the temporal aspects of navigation data. By leveraging attention mechanisms, the proposed approach enhances temporal feature extraction and improves the accuracy of pseudo-UWB observations generation. Extensive experiments demonstrate that the attention-LSTM model significantly reduces positioning errors under both loosely and tightly coupled configurations in NLOS scenarios. This hybrid fusion of model-based and learning-based techniques ensures robust and precise UWB/INS localization.https://doi.org/10.1038/s41598-025-05501-3UWB/INS integrated navigation systemNLOSAttention mechanismLSTMPseudo-observation |
| spellingShingle | Meilin Ren Junyu Wei Jiangyi Qin Xiaojun Guo Haowen Wang Shiqi Li Attention based LSTM framework for robust UWB and INS integration in NLOS environments Scientific Reports UWB/INS integrated navigation system NLOS Attention mechanism LSTM Pseudo-observation |
| title | Attention based LSTM framework for robust UWB and INS integration in NLOS environments |
| title_full | Attention based LSTM framework for robust UWB and INS integration in NLOS environments |
| title_fullStr | Attention based LSTM framework for robust UWB and INS integration in NLOS environments |
| title_full_unstemmed | Attention based LSTM framework for robust UWB and INS integration in NLOS environments |
| title_short | Attention based LSTM framework for robust UWB and INS integration in NLOS environments |
| title_sort | attention based lstm framework for robust uwb and ins integration in nlos environments |
| topic | UWB/INS integrated navigation system NLOS Attention mechanism LSTM Pseudo-observation |
| url | https://doi.org/10.1038/s41598-025-05501-3 |
| work_keys_str_mv | AT meilinren attentionbasedlstmframeworkforrobustuwbandinsintegrationinnlosenvironments AT junyuwei attentionbasedlstmframeworkforrobustuwbandinsintegrationinnlosenvironments AT jiangyiqin attentionbasedlstmframeworkforrobustuwbandinsintegrationinnlosenvironments AT xiaojunguo attentionbasedlstmframeworkforrobustuwbandinsintegrationinnlosenvironments AT haowenwang attentionbasedlstmframeworkforrobustuwbandinsintegrationinnlosenvironments AT shiqili attentionbasedlstmframeworkforrobustuwbandinsintegrationinnlosenvironments |