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| Impact time cooperative guidance law of UAV based onmaneuvering target state estimation |
| WeiZhu1,FengYu2,JinGuo1,WenchaoXue3,4,YanpengHu1,5 |
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| (School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;The Chinese People’s Liberation Army 91614, Dalian 116041, Liaoning, China;State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, Beijing 100083, China) |
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| 摘要: |
| Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance, this paper proposes an impact time cooperative control guidance law (ITCCG) that combines the optimal error dynamics with an improved adaptive cubature Kalman filter (IACKF) algorithm. First, a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model, and terminal time control is achieved through optimal error dynamics. Then, the Huber loss function is used to reduce the impact of measurement outliers, and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition. Finally, the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation. Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame. |
| 关键词: Time constraint · Maneuvering target · Optimal error dynamics · Target estimation · IACKF |
| DOI：https://doi.org/10.1007/s11768-025-00293-3 |
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| 基金项目:This work was supported by the Fundamental Research Funds for the Central Universities of China (FRF-TP-24-058A), with additional support from the National Key Laboratory of Helicopter Aeromechanics (2024-ZSJ-LB-02-02). |
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| Impact time cooperative guidance law of UAV based onmaneuvering target state estimation |
| Wei Zhu1,Feng Yu2,Jin Guo1,Wenchao Xue3,4,Yanpeng Hu1,5 |
| (School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;The Chinese People’s Liberation Army 91614, Dalian 116041, Liaoning, China;State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, Beijing 100083, China) |
| Abstract: |
| Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance, this paper proposes an impact time cooperative control guidance law (ITCCG) that combines the optimal error dynamics with an improved adaptive cubature Kalman filter (IACKF) algorithm. First, a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model, and terminal time control is achieved through optimal error dynamics. Then, the Huber loss function is used to reduce the impact of measurement outliers, and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition. Finally, the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation. Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame. |
| Key words: Time constraint · Maneuvering target · Optimal error dynamics · Target estimation · IACKF |
