Research on Combat Mission Configuration of Unmanned Aerial Vehicle Maritime Reconnaissance Based on Particle Swarm Optimization Algorithm

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Complexity Pub Date : 2024-03-31 DOI:10.1155/2024/9143774
Peng Dong, Weibing Chen, Kewen Wang, Ke Zhou, Wei Wang
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引用次数: 0

Abstract

In recent years, in the classic battles and armed conflicts around the world, battlefield environment reconnaissance and the collection and processing of operational information play an increasingly critical role in the victory and defeat of the battlefield. Unmanned equipment, especially UAV equipment, is used by more and more countries in the field of combat reconnaissance. Meanwhile, the types of UAV are gradually diversified with the change of operational requirements. UAVs adapted to different combat environments shine brightly on the battlefield. In terms of naval battle field, due to the limitations and deficiencies of reconnaissance methods such as surface radar, UAVs play a more prominent role in combat reconnaissance. There are more scenarios for UAVs to be used in combat reconnaissance in naval battle field and higher requirements for UAVs’ combat effectiveness. Therefore, this paper takes UAVs’ naval battle reconnaissance missions as the research object. By using PSO as the research method, this paper studies the combat reconnaissance task configuration of UAVs, hoping to contribute to the improvement of UAVs’ combat reconnaissance capability and combat effectiveness.

基于粒子群优化算法的无人机海上侦察作战任务配置研究
近年来,在世界范围内的经典战役和武装冲突中,战场环境侦察和作战信息的收集与处理对战场的胜负起着越来越关键的作用。无人装备,尤其是无人机装备,被越来越多的国家应用于作战侦察领域。同时,随着作战需求的变化,无人机的类型也逐渐多样化。适应不同作战环境的无人机在战场上大放异彩。就海战领域而言,由于水面雷达等侦察手段的局限性和不足,无人机在作战侦察中的作用更加突出。无人机在海战场作战侦察中的应用场景更多,对无人机的作战效能要求更高。因此,本文以无人机海战侦察任务为研究对象。本文以PSO为研究方法,对无人机作战侦察任务配置进行研究,希望对提高无人机作战侦察能力和作战效能有所帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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