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| Deep Learning-Based Hypersonic Vehicle Motion Behavior Recognition |
| LIN Zhaochen1， ZHANG Xinran1， LIU Ziyang1， HE Fenghua1， OUYANG Lei2 |
| 1. School of Astronautics， Harbin Institute of Technology， Harbin 150001， Heilongjiang， China；
2. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109，China |
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Abstract The hypersonic vehicle has the characteristics of large maneuverability, high speed, long-range and strong threat. It has become a critical weapon development in many countries, and the identification of its motion behavior can significantly support defense and interception. In this study the hypersonic vehicle motion behavior recognition problem under the complex constraints of a no-fly zone was investigated. First, parametric motion equations that can fully describe the behavioral patterns of hypersonic vehicles were developed by analyzing the motion characteristics of hypersonic vehicles， and the trajectory planning problem under the complex constraints was acquired by optimization. Then, the hypersonic vehicle motion behavior recognition algorithm was designed based on deep learning. Finally, simulation experimental results were achieved and analyzed, presenting the effectiveness and generalization ability of the proposed algorithm.
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Received: 16 October 2023
Published: 04 March 2024
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| [1] |
YAN Honglei, LU Yuan, GUO Jie, TANG Shengjing, LI Xiang. Cluster Cooperative Formation Control Method for Underactuated Hypersonic Vehicle[J]. Air & Space Defense, 2024, 7(1): 56-62. |
| [2] |
DI Ziqi, WANG Xiangyu, WU Shuang, ZHOU Yu. An Algorithm for Trajectory Generation and Prediction of Hypersonic Vehicle Based on Transformer Architecture[J]. Air & Space Defense, 2023, 6(4): 35-41. |
| [3] |
. Current Developments in Foreign Hypersonic Vehicles and Defense Systems[J]. Air & Space Defense, 2023, 6(3): 39-51. |
| [4] |
WANG Bing, PI Gang, CHEN Wencheng, XIE Haifeng, SHI Xiangling. Deep Learning Based Surface Defect Detection Method of Flexible Solar Array Hinge[J]. Air & Space Defense, 2023, 6(1): 96-101. |
| [5] |
TIAN Ruocen, ZHANG Qingzhen, GUO Yunhe, CHENG Lin. Design of Reentry Guidance Law of Hypersonic Vehicle Based on No-Fly Zone Avoidance[J]. Air & Space Defense, 2022, 5(2): 65-74. |
| [6] |
LIU Shuangxi, WANG Yichong, ZHU Mengjie, LI Yong, YAN Binbin. Research on Differential Game Guidance Law for Intercepting Hypersonic Vehicles with Small Missile-to-Target Speed Ratio[J]. Air & Space Defense, 2022, 5(2): 49-57. |
| [7] |
JIN Lijie, WU Yatao. Radar Signal Modulation Type Recognition Based on Double CNN[J]. Air & Space Defense, 2022, 5(1): 66-70. |
| [8] |
TAO Haihong, YAN Yingfei. A Netted Radar Node Selection Algorithm Based on GA-CNN[J]. Air & Space Defense, 2022, 5(1): 1-5. |
| [9] |
CAI Yunze, ZHANG Yanjun. Infrared Dim and Small Target Detection Based on Dual-Channel Feature-Enhancement Integrated Attention Network[J]. Air & Space Defense, 2021, 4(4): 14-22. |
| [10] |
XIONG Junhui, LI Keyong, LIU Yi, JI Yu. Study on Near Space Defense Technology Development and Penetration Strategy[J]. Air & Space Defense, 2021, 4(2): 82-. |
| [11] |
WANG Haoning, TANG Shengjing, GUO Jie, HUANG Fan. Time-Constrained Reentry Guidance with Dynamic Angle of Attack Profile[J]. Air & Space Defense, 2021, 4(1): 71-76. |
| [12] |
CAO Jinghao, ZHANG Junju, HUANG Wei, YAO Ruotong, ZHANG Ping. UAV Recognition and Detection Based on Multi-scale Feature Fusion[J]. Air & Space Defense, 2021, 4(1): 60-64. |
| [13] |
CHEN Jichi, WEI Guohua, GUO Conglong, ZHANG Lihe. Preliminary Study on a Deep Learning 3D Reconstruction Simulation Method Based on Infrared Image Sequence[J]. Air & Space Defense, 2020, 3(4): 21-29. |
| [14] |
LIU Haodong, ZHANG Qingzhen, GUO Yunhe, MAO Jiawen. Online Identification of Morphing Aircraft Model Parameters Based on Recursive Least Square Method[J]. Air & Space Defense, 2020, 3(3): 103-110. |
| [15] |
Zhou Lai, Jin Xiaowei, Zheng Yikai. Researchon Operational Decision Support Based on Deep Reinforcement Learning[J]. Air & Space Defense, 2018, 1(1): 31-35. |
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2024, Vol. 7 
