Research on Target Allocation for Hard-Kill Swarm Anti-Unmanned Aerial Vehicle Swarm Systems
In response to the saturated attacks by low, slow, and small UAV swarms, there is currently a lack of effective countermeasures. Counter-UAV swarm technology is an important issue that urgently requires breakthroughs. This paper conducts research on a mid–short-range hard-kill counter-swarm scenario...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/11/666 |
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| author | Jianan Zong Xianzhong Gao Yue Zhang Zhongxi Hou |
| author_facet | Jianan Zong Xianzhong Gao Yue Zhang Zhongxi Hou |
| author_sort | Jianan Zong |
| collection | DOAJ |
| description | In response to the saturated attacks by low, slow, and small UAV swarms, there is currently a lack of effective countermeasures. Counter-UAV swarm technology is an important issue that urgently requires breakthroughs. This paper conducts research on a mid–short-range hard-kill counter-swarm scenario where fewer swarms confront multiple swarms and stronger swarms confront weaker swarms. The requirement is for counter-swarm UAVs to quickly penetrate the swarm at mid–short range and collide with as many incoming UAVs as possible to destroy them. To address the sparse solution space problem, an improved genetic algorithm that integrates multiple strategies is adopted to calculate the spatial density distribution of the incoming swarm. A baseline is identified through gradient descent that maximizes the density integral in a straight-line direction. Based on this baseline, the solution space for single strikes on the swarm is filtered. During the solution process, an elite strategy is introduced to prevent the overall degradation of the population performance. Additionally, the feasibility of the flight trajectory needs to be assessed. A piecewise cubic spline interpolation method is used to optimize the flight trajectory, minimizing the maximum curvature. Ultimately, multiple counter-swarm UAV targets within the swarm and their corresponding trajectories are obtained. |
| format | Article |
| id | doaj-art-9e64d13caae24673bd5fade6589552a6 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-9e64d13caae24673bd5fade6589552a62024-11-26T18:00:46ZengMDPI AGDrones2504-446X2024-11-0181166610.3390/drones8110666Research on Target Allocation for Hard-Kill Swarm Anti-Unmanned Aerial Vehicle Swarm SystemsJianan Zong0Xianzhong Gao1Yue Zhang2Zhongxi Hou3College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaTest Center, National University of Defense Technology, Xi’an 710106, ChinaCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaIn response to the saturated attacks by low, slow, and small UAV swarms, there is currently a lack of effective countermeasures. Counter-UAV swarm technology is an important issue that urgently requires breakthroughs. This paper conducts research on a mid–short-range hard-kill counter-swarm scenario where fewer swarms confront multiple swarms and stronger swarms confront weaker swarms. The requirement is for counter-swarm UAVs to quickly penetrate the swarm at mid–short range and collide with as many incoming UAVs as possible to destroy them. To address the sparse solution space problem, an improved genetic algorithm that integrates multiple strategies is adopted to calculate the spatial density distribution of the incoming swarm. A baseline is identified through gradient descent that maximizes the density integral in a straight-line direction. Based on this baseline, the solution space for single strikes on the swarm is filtered. During the solution process, an elite strategy is introduced to prevent the overall degradation of the population performance. Additionally, the feasibility of the flight trajectory needs to be assessed. A piecewise cubic spline interpolation method is used to optimize the flight trajectory, minimizing the maximum curvature. Ultimately, multiple counter-swarm UAV targets within the swarm and their corresponding trajectories are obtained.https://www.mdpi.com/2504-446X/8/11/666counter-UAVinterception of UAVsswarmtarget allocationtrajectory optimizationcounter-swarm |
| spellingShingle | Jianan Zong Xianzhong Gao Yue Zhang Zhongxi Hou Research on Target Allocation for Hard-Kill Swarm Anti-Unmanned Aerial Vehicle Swarm Systems Drones counter-UAV interception of UAVs swarm target allocation trajectory optimization counter-swarm |
| title | Research on Target Allocation for Hard-Kill Swarm Anti-Unmanned Aerial Vehicle Swarm Systems |
| title_full | Research on Target Allocation for Hard-Kill Swarm Anti-Unmanned Aerial Vehicle Swarm Systems |
| title_fullStr | Research on Target Allocation for Hard-Kill Swarm Anti-Unmanned Aerial Vehicle Swarm Systems |
| title_full_unstemmed | Research on Target Allocation for Hard-Kill Swarm Anti-Unmanned Aerial Vehicle Swarm Systems |
| title_short | Research on Target Allocation for Hard-Kill Swarm Anti-Unmanned Aerial Vehicle Swarm Systems |
| title_sort | research on target allocation for hard kill swarm anti unmanned aerial vehicle swarm systems |
| topic | counter-UAV interception of UAVs swarm target allocation trajectory optimization counter-swarm |
| url | https://www.mdpi.com/2504-446X/8/11/666 |
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