| 通信与电子工程 |
|
|
|
|
| 基于频域双维协同特征的AUV多传感器融合导航 |
韦超( ),陈凌峰,王雨农,鲁仕博,徐元欣*() |
| 浙江大学 信息与电子工程学院，浙江 杭州 310058 |
|
| Bispectral synergetic feature-based multi-sensor fusion navigation for AUV |
| Chao WEI(),Lingfeng CHEN,Yunong WANG,Shibo LU,Yuanxin XU*() |
| College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310058, China |
引用本文:
韦超,陈凌峰,王雨农,鲁仕博,徐元欣. 基于频域双维协同特征的AUV多传感器融合导航[J]. 浙江大学学报(工学版), 2026, 60(12): 2673-2682.
Chao WEI,Lingfeng CHEN,Yunong WANG,Shibo LU,Yuanxin XU. Bispectral synergetic feature-based multi-sensor fusion navigation for AUV. Journal of ZheJiang University (Engineering Science), 2026, 60(12): 2673-2682.
链接本文:
https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2026.12.013
或
https://www.zjujournals.com/eng/CN/Y2026/V60/I12/2673
|
| 1 |
李洋, 赵鸣, 徐梦瑶, 等 多源信息融合技术研究综述[J]. 智能计算机与应用, 2019, 9 (5): 186- 189
Li Yang, Zhao Ming, Xu Mengyao, et al A survey of research on multi-source information fusion technology[J]. Intelligent Computer and Applications, 2019, 9 (5): 186- 189
doi: 10.3969/j.issn.2095-2163.2019.05.043
|
| 2 |
Sun Y, Li J, Xu Y, et al Deep learning versus conventional methods for missing data imputation: a review and comparative study[J]. Expert Systems with Applications, 2023, 227: 120201
doi: 10.1016/j.eswa.2023.120201
|
| 3 |
Deraz A A, Badawy O, Elhosseini M A, et al Deep learning based on LSTM model for enhanced visual odometry navigation system[J]. Ain Shams Engineering Journal, 2023, 14 (8): 102050
doi: 10.1016/j.asej.2022.102050
|
| 4 |
Zhi Z, Liu D, Liu L A performance compensation method for GPS/INS integrated navigation system based on CNN-LSTM during GPS outages[J]. Measurement, 2022, 188: 110516
doi: 10.1016/j.measurement.2021.110516
|
| 5 |
Tng Y, Jiang J, Liu J, et al A GRU and AKF-based hybrid algorithm for improving INS/GNSS navigation accuracy during GNSS outage[J]. Remote Sensing, 2022, 14 (3): 752
doi: 10.3390/rs14030752
|
| 6 |
Liu Y, Hu T, Zhang H, et al. iTransformer: inverted Transformers are effective for time series forecasting [C]//The 12th International Conference on Learning Representations, 2024: 1−25.
|
| 7 |
Liu Y, Wu H, Wang J, et al. Non-stationary Transformers: exploring the stationarity in time series forecasting [C]//Advances in Neural Information Processing Systems 35. Red Hook, NY: Curran Associates, Inc. , 2022: 9881-9893.
|
| 8 |
Nie Y, Nguyen N H, Sinthong P, et al. A time series is worth 64 words: long-term forecasting with Transformers [C]//The 11th International Conference on Learning Representations, 2023: 1−24.
|
| 9 |
Hochreiter S, Schmidhuber J Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780
doi: 10.1162/neco.1997.9.8.1735
|
| 10 |
Lai G, Chang W C, Yang Y, et al. Modeling long- and short-term temporal patterns with deep neural networks [C]//The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2018: 95-104.
|
| 11 |
Shen L, Li Z, Kwok J T. Timeseries anomaly detection using temporal hierarchical one-class network [C]//Advances in Neural Information Processing Systems 33. Red Hook, NY: Curran Associates, Inc. , 2020: 13016-13026.
|
| 12 |
Bai S, Kolter J Z, Koltun V. Convolutional sequence modeling revisited [C]//Proceedings of the 6th International Conference on Learning Representations Workshop, 2018: 1−20.
|
| 13 |
He Y, Zhao J Temporal convolutional networks for anomaly detection in time series[J]. Journal of Physics: Conference Series, 2019, 1213 (4): 042050
doi: 10.1088/1742-6596/1213/4/042050
|
| 14 |
Wu H, Hu T, Liu Y, et al. TimesNet: temporal 2D-variation modeling for general time series analysis [C]//The 11th International Conference on Learning Representations, 2023: 1−23.
|
| 15 |
Wang S, Wu H, Shi X, et al. TimeMixer: decomposable multiscale mixing for time series forecasting [C]//The 12th International Conference on Learning Representations, 2024: 1−27.
|
| 16 |
Wang Y, Wu H, Dong J, et al. TimeXer: empowering Transformers for time series forecasting with exogenous variables [C]//Advances in Neural Information Processing Systems 37. Red Hook, NY: Curran Associates, Inc. , 2024: 469-498.
|
| 17 |
Zeng A, Chen M, Zhang L, et al. Are Transformers effective for time series forecasting? [C]//Proceedings of the 37th AAAI Conference on Artificial Intelligence. Washington, DC: AAAI Press, 2023, 37(9): 11121-11128.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
