Optimization of False Target Jamming against UAV Detection
Unmanned aerial vehicles (UAVs) have been widely used for target detection in modern battlefields. From the viewpoint of the opponents, false target jamming is an effective approach to decrease the UAV detection ability or probability, but currently there are few research efforts devoted to this adv...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/6/5/114 |
| _version_ | 1797500341181218816 |
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| author | Zheng-Lian Su Xun-Lin Jiang Ning Li Hai-Feng Ling Yu-Jun Zheng |
| author_facet | Zheng-Lian Su Xun-Lin Jiang Ning Li Hai-Feng Ling Yu-Jun Zheng |
| author_sort | Zheng-Lian Su |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) have been widely used for target detection in modern battlefields. From the viewpoint of the opponents, false target jamming is an effective approach to decrease the UAV detection ability or probability, but currently there are few research efforts devoted to this adversarial problem. This paper formulates an optimization problem of false target jamming based on a counterpart problem of UAV detection, where each false target jamming solution is evaluated according to its adversarial effects on a set of possible UAV detection solutions. To efficiently solve the problem, we propose an evolutionary framework, which is implemented with four popular evolutionary algorithms by designing/adapting their evolutionary operators for false target jamming solutions. Experimental results on 12 test instances with different search regions and numbers of UAVs and false targets demonstrate that the proposed approach can significantly reduce the UAV detection probability, and the water wave optimization (WWO) metaheuristic exhibits the best overall performance among the four evolutionary algorithms. To our knowledge, this is the first study on the optimization of false target jamming against UAV detection, and the proposed framework can be extended to more countermeasures against UAV operations. |
| first_indexed | 2024-03-10T03:00:32Z |
| format | Article |
| id | doaj.art-0000c89e81ec4605be0f000c1cd234f8 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T03:00:32Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-0000c89e81ec4605be0f000c1cd234f82023-11-23T10:44:10ZengMDPI AGDrones2504-446X2022-05-016511410.3390/drones6050114Optimization of False Target Jamming against UAV DetectionZheng-Lian Su0Xun-Lin Jiang1Ning Li2Hai-Feng Ling3Yu-Jun Zheng4College of Field Engineering, Army Engineering University, Nanjing 210007, ChinaDepartment of Engineering Technology and Application, Army Infantry College, Nanchang 330100, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaCollege of Field Engineering, Army Engineering University, Nanjing 210007, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaUnmanned aerial vehicles (UAVs) have been widely used for target detection in modern battlefields. From the viewpoint of the opponents, false target jamming is an effective approach to decrease the UAV detection ability or probability, but currently there are few research efforts devoted to this adversarial problem. This paper formulates an optimization problem of false target jamming based on a counterpart problem of UAV detection, where each false target jamming solution is evaluated according to its adversarial effects on a set of possible UAV detection solutions. To efficiently solve the problem, we propose an evolutionary framework, which is implemented with four popular evolutionary algorithms by designing/adapting their evolutionary operators for false target jamming solutions. Experimental results on 12 test instances with different search regions and numbers of UAVs and false targets demonstrate that the proposed approach can significantly reduce the UAV detection probability, and the water wave optimization (WWO) metaheuristic exhibits the best overall performance among the four evolutionary algorithms. To our knowledge, this is the first study on the optimization of false target jamming against UAV detection, and the proposed framework can be extended to more countermeasures against UAV operations.https://www.mdpi.com/2504-446X/6/5/114unmanned aerial vehicle (UAV)UAV detectionfalse target jammingoptimizationevolutionary algorithm |
| spellingShingle | Zheng-Lian Su Xun-Lin Jiang Ning Li Hai-Feng Ling Yu-Jun Zheng Optimization of False Target Jamming against UAV Detection Drones unmanned aerial vehicle (UAV) UAV detection false target jamming optimization evolutionary algorithm |
| title | Optimization of False Target Jamming against UAV Detection |
| title_full | Optimization of False Target Jamming against UAV Detection |
| title_fullStr | Optimization of False Target Jamming against UAV Detection |
| title_full_unstemmed | Optimization of False Target Jamming against UAV Detection |
| title_short | Optimization of False Target Jamming against UAV Detection |
| title_sort | optimization of false target jamming against uav detection |
| topic | unmanned aerial vehicle (UAV) UAV detection false target jamming optimization evolutionary algorithm |
| url | https://www.mdpi.com/2504-446X/6/5/114 |
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