Res-EMSA: Adaptive Adjustment of Innovation Based on Efficient Multihead Self-Attention in GNSS/INS Tightly Integrated Navigation System
文献类型：期刊论文
| 作者 | Xu, Hongfu2; Luo, Haiyong1; Wu, Zijian2; Zhao, Fang2 |
| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2024 |
| 卷号 | 21页码:5 |
| 关键词 | Technological innovation Global navigation satellite system Navigation Feature extraction Adaptation models Measurement uncertainty Kalman filters Deep learning extended Kalman filter (EKF) innovation matrix integrated navigation |
| ISSN号 | 1545-598X |
| DOI | 10.1109/LGRS.2024.3365148 |
| 英文摘要 | Tightly integrated navigation based on an extended Kalman filter (EKF) has become ubiquitous across domains such as autonomous driving. The accuracy of the innovation matrix plays a crucial role in the filtering process. However, the variance in data quality across different satellites due to various errors, as well as the nonlinear errors introduced by the linearization of the measurement equation and the errors resulting from the Gaussian noise assumption, can lead to inaccuracies in the innovation matrix. We address these aforementioned issues and propose an adaptive solution relying on Resnet-efficient multihead self-attention (Res-EMSA) to adjust the innovation matrix. Specifically, the network model extracts the features of the global navigation satellite system (GNSS) and inertial navigation system (INS) data through the residual network, and then the features are fused with different weights. After that, the EMSA network is utilized for feature mapping, and ultimately, the fully connected layer is used to perform weight matrix regression. Experimental results reveal that the Res-EMSA model exhibits a significant enhancement in positioning accuracy, with a 39% increase compared to the traditional EKF model. |
| 资助项目 | National Key Research and Development Program |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001173135800024 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/38782]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Luo, Haiyong; Zhao, Fang |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Res Ctr Ubiquitous Comp Syst, Beijing 100086, Peoples R China 2.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Hongfu,Luo, Haiyong,Wu, Zijian,et al. Res-EMSA: Adaptive Adjustment of Innovation Based on Efficient Multihead Self-Attention in GNSS/INS Tightly Integrated Navigation System[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2024,21:5. |
| APA | Xu, Hongfu,Luo, Haiyong,Wu, Zijian,&Zhao, Fang.(2024).Res-EMSA: Adaptive Adjustment of Innovation Based on Efficient Multihead Self-Attention in GNSS/INS Tightly Integrated Navigation System.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,21,5. |
| MLA | Xu, Hongfu,et al."Res-EMSA: Adaptive Adjustment of Innovation Based on Efficient Multihead Self-Attention in GNSS/INS Tightly Integrated Navigation System".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 21(2024):5. |
入库方式： OAI收割
来源：计算技术研究所
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