Enhancing Integrated Navigation with a Self-Attention LSTM Hybrid Network for UAVs in GNSS-Denied Environments
Performing long-duration navigation without the global navigation satellite system (GNSS) network is a challenging task, particularly for small unmanned aerial vehicles (UAVs) equipped with low-cost micro-electro-mechanical sensors. This study proposes a hybrid neural network that integrates self-at...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/4/279 |
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| author | Ziyi Wang Xiaojun Shen Jie Li Juan Li Xueyong Wu Yu Yang |
| author_facet | Ziyi Wang Xiaojun Shen Jie Li Juan Li Xueyong Wu Yu Yang |
| author_sort | Ziyi Wang |
| collection | DOAJ |
| description | Performing long-duration navigation without the global navigation satellite system (GNSS) network is a challenging task, particularly for small unmanned aerial vehicles (UAVs) equipped with low-cost micro-electro-mechanical sensors. This study proposes a hybrid neural network that integrates self-attention mechanisms with long short-term memory (SALSTM) to enhance GNSS-denied navigation performance. The estimation task of GNSS-denied navigation is first modeled based on UAV aerodynamics and kinematics, enabling a precise definition of the inputs and outputs that SALSTM needs to map. A self-attention layer is inserted in multiple LSTM layers to capture long-range dependencies in subtle dynamic changes. The output layer is designed to generate state sequences, leveraging the recursive nature of LSTM to enforce state continuity constraints. The outputs of SALSTM are fused to enhance integrated navigation within an extended Kalman filter framework. The performance of the proposed method is evaluated using flight data obtained from field tests. The results demonstrate that SALSTM-enhanced integrated navigation achieves superior long-term stability and improves velocity and position estimation accuracy by more than 50% compared to the best existing methods. |
| format | Article |
| id | doaj-art-e7dcba2ad16947e78fd957789e823f2d |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-e7dcba2ad16947e78fd957789e823f2d2025-08-20T02:28:12ZengMDPI AGDrones2504-446X2025-04-019427910.3390/drones9040279Enhancing Integrated Navigation with a Self-Attention LSTM Hybrid Network for UAVs in GNSS-Denied EnvironmentsZiyi Wang0Xiaojun Shen1Jie Li2Juan Li3Xueyong Wu4Yu Yang5School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaBeijing Special Electromechanical Research Institute, Beijing 100012, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaYangtze Delta Region Academy of Beijing Institute of Technology, No. 1940, East North Road, Youchegang Town, Xiuzhou District, Jiaxing 314019, ChinaPerforming long-duration navigation without the global navigation satellite system (GNSS) network is a challenging task, particularly for small unmanned aerial vehicles (UAVs) equipped with low-cost micro-electro-mechanical sensors. This study proposes a hybrid neural network that integrates self-attention mechanisms with long short-term memory (SALSTM) to enhance GNSS-denied navigation performance. The estimation task of GNSS-denied navigation is first modeled based on UAV aerodynamics and kinematics, enabling a precise definition of the inputs and outputs that SALSTM needs to map. A self-attention layer is inserted in multiple LSTM layers to capture long-range dependencies in subtle dynamic changes. The output layer is designed to generate state sequences, leveraging the recursive nature of LSTM to enforce state continuity constraints. The outputs of SALSTM are fused to enhance integrated navigation within an extended Kalman filter framework. The performance of the proposed method is evaluated using flight data obtained from field tests. The results demonstrate that SALSTM-enhanced integrated navigation achieves superior long-term stability and improves velocity and position estimation accuracy by more than 50% compared to the best existing methods.https://www.mdpi.com/2504-446X/9/4/279fixed-wing UAVintegrated navigationGNSS-deniedself-attentionLSTM |
| spellingShingle | Ziyi Wang Xiaojun Shen Jie Li Juan Li Xueyong Wu Yu Yang Enhancing Integrated Navigation with a Self-Attention LSTM Hybrid Network for UAVs in GNSS-Denied Environments Drones fixed-wing UAV integrated navigation GNSS-denied self-attention LSTM |
| title | Enhancing Integrated Navigation with a Self-Attention LSTM Hybrid Network for UAVs in GNSS-Denied Environments |
| title_full | Enhancing Integrated Navigation with a Self-Attention LSTM Hybrid Network for UAVs in GNSS-Denied Environments |
| title_fullStr | Enhancing Integrated Navigation with a Self-Attention LSTM Hybrid Network for UAVs in GNSS-Denied Environments |
| title_full_unstemmed | Enhancing Integrated Navigation with a Self-Attention LSTM Hybrid Network for UAVs in GNSS-Denied Environments |
| title_short | Enhancing Integrated Navigation with a Self-Attention LSTM Hybrid Network for UAVs in GNSS-Denied Environments |
| title_sort | enhancing integrated navigation with a self attention lstm hybrid network for uavs in gnss denied environments |
| topic | fixed-wing UAV integrated navigation GNSS-denied self-attention LSTM |
| url | https://www.mdpi.com/2504-446X/9/4/279 |
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