Efficient and optimal penetration path planning for stealth unmanned aerial vehicle using minimal radar cross-section tactics and modified A-Star algorithm
Penetration path planning for stealth unmanned aerial vehicles (SUAVs) in the integrated air defense system (IADS) has been a hot research topic in recent years. The present study examines penetration path planning in different threat environments. Firstly, for the complex terrain and static radar t...
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| Published in | ISA transactions Vol. 134; pp. 42 - 57 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.03.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0019-0578 1879-2022 1879-2022 |
| DOI | 10.1016/j.isatra.2022.07.032 |
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| Abstract | Penetration path planning for stealth unmanned aerial vehicles (SUAVs) in the integrated air defense system (IADS) has been a hot research topic in recent years. The present study examines penetration path planning in different threat environments. Firstly, for the complex terrain and static radar threats, a modified A-Star algorithm containing the bidirectional sector expansion and variable step search strategy is proposed to elude static threats rapidly. Then, with regard to bandit threats, the minimal radar cross-section (RCS) tactics are presented to achieve path replanning. Furthermore, the combinatorial methodology of the minimum RCS tactics and the modified A-Star algorithm is applied to achieve the dynamic path planning for SUAV. The simulation results indicate that the modified A-Star algorithm and minimal RCS tactics can significantly reduce the probability of radar system, which has better superiority in calculation efficiency, path cost and safety. And the minimal RCS tactics have better real-time performance and are more convenient in dealing with dynamic threats, which enhances the survivability of SUAV and verifies the effectiveness of the proposed methodology.
•This paper presents a novel framework to settle dynamic path planning problem for stealth unmanned aerial vehicles.•A modified A-Star algorithm that adopts the bidirectional sector variable step search strategy.•The minimal radar cross-section tactics are convenient to deal with the bandit threats and enhance real-time performance.•The combinatorial methodology has better superiority in calculation efficiency, path cost and safety. |
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| AbstractList | Penetration path planning for stealth unmanned aerial vehicles (SUAVs) in the integrated air defense system (IADS) has been a hot research topic in recent years. The present study examines penetration path planning in different threat environments. Firstly, for the complex terrain and static radar threats, a modified A-Star algorithm containing the bidirectional sector expansion and variable step search strategy is proposed to elude static threats rapidly. Then, with regard to bandit threats, the minimal radar cross-section (RCS) tactics are presented to achieve path replanning. Furthermore, the combinatorial methodology of the minimum RCS tactics and the modified A-Star algorithm is applied to achieve the dynamic path planning for SUAV. The simulation results indicate that the modified A-Star algorithm and minimal RCS tactics can significantly reduce the probability of radar system, which has better superiority in calculation efficiency, path cost and safety. And the minimal RCS tactics have better real-time performance and are more convenient in dealing with dynamic threats, which enhances the survivability of SUAV and verifies the effectiveness of the proposed methodology. Penetration path planning for stealth unmanned aerial vehicles (SUAVs) in the integrated air defense system (IADS) has been a hot research topic in recent years. The present study examines penetration path planning in different threat environments. Firstly, for the complex terrain and static radar threats, a modified A-Star algorithm containing the bidirectional sector expansion and variable step search strategy is proposed to elude static threats rapidly. Then, with regard to bandit threats, the minimal radar cross-section (RCS) tactics are presented to achieve path replanning. Furthermore, the combinatorial methodology of the minimum RCS tactics and the modified A-Star algorithm is applied to achieve the dynamic path planning for SUAV. The simulation results indicate that the modified A-Star algorithm and minimal RCS tactics can significantly reduce the probability of radar system, which has better superiority in calculation efficiency, path cost and safety. And the minimal RCS tactics have better real-time performance and are more convenient in dealing with dynamic threats, which enhances the survivability of SUAV and verifies the effectiveness of the proposed methodology. •This paper presents a novel framework to settle dynamic path planning problem for stealth unmanned aerial vehicles.•A modified A-Star algorithm that adopts the bidirectional sector variable step search strategy.•The minimal radar cross-section tactics are convenient to deal with the bandit threats and enhance real-time performance.•The combinatorial methodology has better superiority in calculation efficiency, path cost and safety. Penetration path planning for stealth unmanned aerial vehicles (SUAVs) in the integrated air defense system (IADS) has been a hot research topic in recent years. The present study examines penetration path planning in different threat environments. Firstly, for the complex terrain and static radar threats, a modified A-Star algorithm containing the bidirectional sector expansion and variable step search strategy is proposed to elude static threats rapidly. Then, with regard to bandit threats, the minimal radar cross-section (RCS) tactics are presented to achieve path replanning. Furthermore, the combinatorial methodology of the minimum RCS tactics and the modified A-Star algorithm is applied to achieve the dynamic path planning for SUAV. The simulation results indicate that the modified A-Star algorithm and minimal RCS tactics can significantly reduce the probability of radar system, which has better superiority in calculation efficiency, path cost and safety. And the minimal RCS tactics have better real-time performance and are more convenient in dealing with dynamic threats, which enhances the survivability of SUAV and verifies the effectiveness of the proposed methodology.Penetration path planning for stealth unmanned aerial vehicles (SUAVs) in the integrated air defense system (IADS) has been a hot research topic in recent years. The present study examines penetration path planning in different threat environments. Firstly, for the complex terrain and static radar threats, a modified A-Star algorithm containing the bidirectional sector expansion and variable step search strategy is proposed to elude static threats rapidly. Then, with regard to bandit threats, the minimal radar cross-section (RCS) tactics are presented to achieve path replanning. Furthermore, the combinatorial methodology of the minimum RCS tactics and the modified A-Star algorithm is applied to achieve the dynamic path planning for SUAV. The simulation results indicate that the modified A-Star algorithm and minimal RCS tactics can significantly reduce the probability of radar system, which has better superiority in calculation efficiency, path cost and safety. And the minimal RCS tactics have better real-time performance and are more convenient in dealing with dynamic threats, which enhances the survivability of SUAV and verifies the effectiveness of the proposed methodology. |
| Author | Zhu, Xiaozhou Zhang, Zhe Wu, Jian Jiang, Ju |
| Author_xml | – sequence: 1 givenname: Zhe orcidid: 0000-0002-3761-0000 surname: Zhang fullname: Zhang, Zhe email: zhangzhe1205@nuaa.edu.cn organization: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China – sequence: 2 givenname: Ju surname: Jiang fullname: Jiang, Ju email: jiangju@nuaa.edu.cn organization: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China – sequence: 3 givenname: Jian surname: Wu fullname: Wu, Jian organization: College of Information Engineering, Nanchang Hangkong University, Nanchang, 330063, China – sequence: 4 givenname: Xiaozhou surname: Zhu fullname: Zhu, Xiaozhou organization: National Innovation Institute of Defence Technology, Academy of Military Science of the People’s Liberation Army, Beijing, 100071, China |
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| Keywords | Path planning Minimal radar cross-section (RCS) tactics Moving target A-Star algorithm Stealth unmanned aerial vehicles (SUAVs) |
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| SubjectTerms | A-Star algorithm Minimal radar cross-section (RCS) tactics Moving target Path planning Stealth unmanned aerial vehicles (SUAVs) |
| Title | Efficient and optimal penetration path planning for stealth unmanned aerial vehicle using minimal radar cross-section tactics and modified A-Star algorithm |
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