Research on Maneuvering State Recognition Method of Hypersonic Glide Vehicle

ZHANG Junbiao; XIONG Jiajun; LAN Xuhui; CHEN Xin; LI Fan

doi:10.11999/JEIT211009

Volume 44 Issue 12

Dec. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(12): 4134-4143

ZHANG Junbiao, XIONG Jiajun, LAN Xuhui, CHEN Xin, LI Fan. Research on Maneuvering State Recognition Method of Hypersonic Glide Vehicle[J]. Journal of Electronics & Information Technology, 2022, 44(12): 4134-4143. doi: 10.11999/JEIT211009

Citation:

ZHANG Junbiao, XIONG Jiajun, LAN Xuhui, CHEN Xin, LI Fan. Research on Maneuvering State Recognition Method of Hypersonic Glide Vehicle[J]. Journal of Electronics & Information Technology, 2022, 44(12): 4134-4143. doi: 10.11999/JEIT211009

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Research on Maneuvering State Recognition Method of Hypersonic Glide Vehicle

doi: 10.11999/JEIT211009

1.
Early Warning Intelligence Department, Air Force Early Warning Academy, Wuhan 430019, China
2.
Unit 95980 of PLA, Xiangyang 441000, China

Funds: The Military Postgraduate Funding Project of China (JY2019B138, JY2018A039)

Received Date: 2021-09-22
Rev Recd Date: 2022-03-17

Available Online: 2022-04-21

Publish Date: 2022-12-10

Abstract

Abstract

The rapid development of Hypersonic Glide Vehicle (HGV) has changed the traditional combat style and opened a new field of military struggle. Identifying the maneuvering state of HGV can provide a powerful support for threat assessment, trajectory prediction and defense decision. In order to improve the accuracy of HGV maneuver state recognition, an HGV maneuver state recognition model based on ATtention Convolutional Long Short-Term Memory network (AT-ConvLSTM) is proposed. First, on the basis of maneuvering modeling and characteristic analysis of HGV, the maneuvering state of HGV in space is divided into eight categories, and the corresponding feature recognition parameters are constructed. A trajectory library containing HGV maneuvering trajectories under different initial conditions and control modes is established. Then, the conversion steps from radar tracking information to feature recognition parameters are deduced. The proposed state recognition model is used to extract the spatial features of HGV motion trajectory, and the maneuvering state is classified by the SoftMax classifier. Finally, the algorithm is verified by simulation experiments. The results show that the proposed method can effectively identify HGV maneuvering state online, which has good real-time and accuracy.
- Hypersonic,
- Vehicle,
- State recognition,
- Deep learning,
- Maneuver modeling

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References(24)

References

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