基于粒子群算法的重载无人机地面打击策略
Heavy-Duty UAV Ground Strike Strategy Based on Particle Swarm Algorithm
摘要: 未来战场上,重载无人机能够发挥重要作用,包括侦察、掩护、运输、打击等作战场景。面对复杂的作战环境,能对被打击目标快速识别显得尤为重要。在战场环境中地面目标比较复杂,需要对目标精准识别打击,本文通过粒子群算法对识别方法分配,提高重载无人机识别目标的可靠性,并根据危险等级提供打击策略,能达到真实战场要求。
Abstract: In the future battlefield, heavy-duty UAVs can play an important role, including reconnaissance, cover, transportation, attack and other operational scenarios. In the face of a complex combat environment, it is particularly important to identify the target quickly. In the battlefield environment, the ground targets are complex and need to be accurately identified and attacked. This paper uses particle swarm optimization algorithms to allocate identification methods, improve the reliability of heavy-duty UAV target identification and provide attack strategies according to the danger level, which can meet the requirements of the real battlefield.
文章引用:孙磊, 张书绘, 何坚强, 张春富, 辅小荣, 夏菽兰. 基于粒子群算法的重载无人机地面打击策略[J]. 软件工程与应用, 2022, 11(6): 1255-1263. https://doi.org/10.12677/SEA.2022.116128

参考文献

[1] 秦清, 徐毓. 低慢小目标多装备协同探测分配问题研究[J]. 空军雷达学院学报, 2012, 26(1): 28-31.
[2] 李良福, 陈卫东, 高强, 许开銮, 刘轩, 何曦, 钱钧. 基于深度学习的光电系统智能目标识别[J]. 兵工学报, 2022, 43(S1): 162-168.
[3] 王玉杰, 唐钟南, 陈清阳, 高显忠, 邓小龙. 多无人机协同打击制导控制技术研究进展[J/OL]. 航空工程进展: 1-11.
http://kns.cnki.net/kcms/detail/61.1479.V.20220613.1337.006.html, 2022-07-23.
[4] 朱霸坤, 朱卫纲, 李伟, 李佳芯, 杨莹. 基于规划步数自适应Dyna-Q的多功能雷达干扰决策方法[J]. 兵工自动化, 2022, 41(7): 1-4.
[5] 殷宗迪, 何平, 宋秋冬, 朱猛. 识别无人机的无人值守光电告警系统[J]. 飞控与探测, 2018, 1(3): 28-33.
[6] 张瑞鹏, 冯彦翔, 杨宜康. 多无人机协同任务分配混合粒子群算法[J/OL]. 航空学报: 1-15.
https://kns.cnki.net/kcms/detail/11.1929.V.20210906.1259.020.html, 2022-07-23.
[7] 鲁希团, 吕慧, 荆鹏飞, 王健. 无人机侦察目标位置拾取器的设计与实现[J]. 兵器装备工程学报, 2020, 41(4): 138-142.
[8] 张云飞, 林德福, 郑多, 程子恒, 唐攀. 多目标时空同步协同攻击无人机任务分配与轨迹优化[J]. 兵工学报, 2021, 42(7): 1482-1495.
https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2021&filename=BIGO202107016&uniplatform=NZKPT&v=iX0uUdiXZtyumoPJwgrtip0kkEAV_x45Aq4DZcX5lMgcpc9uI9pezAOlsfI3-VbN
[9] 石文君, 刘万锁. 人工智能和知识图谱在无人机智能作战中的应用[J]. 红外, 2020, 41(8): 44-48.
[10] Zhang, S., Zhang, H., Di, B. and Song, L. (2019) Cellular UAV-to-X Communications: Design and Optimization for Multi-UAV Networks. IEEE Transactions on Wireless Communications, 18, 1346-1359. [Google Scholar] [CrossRef
[11] Nikolos, I.K., Valavanis, K.P., Tsourveloudis, N.C. and Kostaras, A.N. (2003) Evolutionary Algorithm Based Offline/Online Path Planner for UAV Navigation. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 33, 898-912. [Google Scholar] [CrossRef
[12] Liu, Y., Qin, Z., Cai, Y., Gao, Y., Li, G.Y. and Nallanathan, A. (2019) UAV Communications Based on Non-Orthogonal Multiple Access. IEEE Wireless Communications, 26, 52-57. [Google Scholar] [CrossRef
[13] Cui, J., Liu, Y. and Nallanathan, A. (2020) Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks. IEEE Transactions on Wireless Communications, 19, 729-743. [Google Scholar] [CrossRef
[14] Liu, M., Wang, X., Zhou, A., Fu, X., Ma, Y. and Piao, C. (2020) UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective. Sensors, 20, Article No. 2238. [Google Scholar] [CrossRef] [PubMed]
[15] Nazib, R.A. and Moh, S. (2021) Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains. IEEE Access, 9, 23168-23190. [Google Scholar] [CrossRef
[16] Li, Y., Fu, C., Ding, F., Huang, Z. and Lu, G. (2020) AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 13-19 June 2020, 11923-11932. [Google Scholar] [CrossRef
[17] Mittal, P., Singh, R. and Akashdeep Sharma, A. (2020) Deep Learning-Based Object Detection in Low-Altitude UAV Datasets: A Survey. Image and Vision Computing, 104, Article No. 104046. [Google Scholar] [CrossRef