التفاصيل البيبلوغرافية
| العنوان: |
Research on trajectory prediction of UAV drone swarm |
| المؤلفون: |
ZHANG Genyuan, LIN Zhiwei, TANG Xu, LEI Kaiwen |
| المصدر: |
Hangkong gongcheng jinzhan, Vol 14, Iss 3, Pp 69-76 (2023) |
| بيانات النشر: |
Editorial Department of Advances in Aeronautical Science and Engineering, 2023. |
| سنة النشر: |
2023 |
| المجموعة: |
LCC:Motor vehicles. Aeronautics. Astronautics |
| مصطلحات موضوعية: |
uav drone swarm, trajectory prediction, fractal algorithm, kalman filter, lstm network, Motor vehicles. Aeronautics. Astronautics, TL1-4050 |
| الوصف: |
The trajectory prediction model of traditional weapon control algorithm cannot effectively predict the complex trajectory of drones. Besides, the single UAV is usually considered in current research on complex trajec-tory prediction, which has huge amount of calculation. To predict the trajectory of UAV drone swarm quickly and accurately, a method of trajectory prediction for UAVs is proposed. After obtaining the trajectory of drone swarm,the clustering is conducted firstly based on DBSCAN(density-based spatial clustering of applications with noise)method to judge the category of each UAV in drone swarm. Then the trajectory complexity of UAV is judged based on fractal algorithm. Finally, Kalman filter is used for simple trajectory prediction, and the long short-term memory (LSTM) network method is used for complex trajectory prediction. The results show that the prediction error of the proposed trajectory prediction method for UAVs is less than that of Kalman filter, and the prediction time is less than that of LSTM network method, which can predict the trajectory of different swarm UAVs in drone, and provide scientific basis for anti-UAV swarm calculation. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
Chinese |
| تدمد: |
1674-8190 |
| Relation: |
http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2022151?st=article_issue; https://doaj.org/toc/1674-8190 |
| DOI: |
10.16615/j.cnki.1674-8190.2023.03.07 |
| URL الوصول: |
https://doaj.org/article/56c6197082f549c1aff5f6b0a930f7bc |
| رقم الانضمام: |
edsdoj.56c6197082f549c1aff5f6b0a930f7bc |
| قاعدة البيانات: |
Directory of Open Access Journals |