摘要
【目的】随着无人机群在侦察任务中的广泛应用,优化防空反制系统部署已成为提升防御能力的重要手段。无人机群凭借其高灵活性、强生存能力和低成本特性对传统防空体系构成了严重威胁。单一防空系统难以有效应对多目标协同的无人机群,因此,需要通过多系统协同部署,最大化无人机群的飞行成本,迫使其改变路径或放弃任务。本研究旨在设计一种高效的防空反制系统部署方法,以应对无人机群侦察带来的安全挑战。【方法】研究提出了一种基于水波优化(water wave optimization,WWO)和A^(*)算法的防空反制系统部署(water wave and A^(*)deployment,WAD)方法,该方法通过两个核心子模型实现优化:一是构建无人机群的最优路径规划模型,用于计算在给定防空反制系统位置下无人机群的最小飞行成本;二是设计防空反制系统选址优化模型,通过调整系统位置来最大化无人机群的期望飞行成本。WAD算法融合了WWO在全局和局部搜索中的平衡优势以及A^(*)算法在路径规划中的高效性,并通过改进的编解码机制提升了搜索效率,避免其陷入低效解空间。【结果】通过仿真实验验证WAD算法的有效性。实验设计了一个包含4个飞行起点、39个路径点和3个防空反制系统的场景,结果表明WAD算法能够求解出无人机群期望飞行成本的最大值,并输出优化的防空反制系统部署位置及无人机群的飞行路径。种群最佳适应度随迭代次数的增大而快速收敛,平均在30次迭代内达到稳定,表明算法具有较高的精度和计算效率,与传统方法相比,显著缩短了优化时间。【结论】WAD算法为无人机群防空反制系统的优化部署提供了一种高效解决方案。通过集成WWO和A^(*)算法的优势,该方法在全局探索与局部开发之间实现了良好平衡,显著提升了部署方案的收敛速度和优化质量。研究结果表明,本文方法适用于复杂侦察场景下的防御需求。未来可进一步研究动态环境下多目标优化的部署策略,探索防空反制系统间的协同机制,引入实时威胁评估,以适应无人机群技术的快速演变。
[Objective]Given the widespread use of drone swarms in reconnaissance missions,optimizing the deployment of air defense countermeasure systems has become a critical issue for enhancing defensive capabilities.Drone swarms,with their high flexibility,robust survivability,and cost-effectiveness,pose a significant threat to traditional air defense frameworks.A single air defense system struggles to effectively address the coordinated multi-target nature of drone swarms,necessitating the collaborative deployment of multiple systems to maximize the flight cost of the swarm,thereby forcing path alterations or mission abandonment.This study aims to develop an efficient deployment method for air defense countermeasure systems to mitigate the security challenges posed by drone swarm reconnaissance.[Methods]This study introduced a deployment method for air defense countermeasure systems against drone swarms,based on water wave optimization(WWO)and the A^(*)algorithm,termed the water wave and A^(*)deployment(WAD)algorithm.The approach integrated two key sub-models:first,an optimal path planning model for drone swarms,which calculated the minimum flight cost under specified air defense countermeasure system positions;second,an air defense countermeasure system location optimization model that adjusted system positions to maximize the expected flight cost of the swarm.The WAD algorithm leveraged WWO′s balanced global and local search capabilities alongside the A^(*)algorithm′s efficiency in path planning,enhanced by an improved encoding-decoding scheme to boost search efficiency and avoid suboptimal solution spaces.[Results]The effectiveness of the WAD algorithm is confirmed through simulation experiments.The experimental scenario includes 4 flight starting points,39 waypoints,and 3 air defense countermeasure systems.Results demonstrate that the WAD algorithm is able to obtain the maximum expected flight cost for the drone swarm and output optimized deployment positions for the air defense countermeasure systems and the swarm′s flight paths.The population′s best fitness converges rapidly with the increase of iteration counts,stabilizing within an average of 30 iterations,highlighting the algorithm′s high precision and computational efficiency,and significantly shortening the optimization time compared with traditional methods.[Conclusions]The WAD algorithm provides an efficient solution for optimizing the deployment of air defense countermeasure systems against drone swarms.By integrating the strengths of WWO and the A^(*)algorithm,it achieves an effective balance between global exploration and local exploitation,markedly improving convergence speed and optimization quality.The findings indicate that this method is applicable to defense requirements in complex reconnaissance scenarios.Future work may further investigates multi-objective optimization strategies in dynamic environments,explores coordination mechanisms among air defense countermeasure systems,and incorporates real-time threat assessment to adapt to the rapid evolution of drone swarm technologies.
作者
李翔
罗望春
张福
张兴华
刘洪驿
LI Xiang;LUO Wangchun;ZHANG Fu;ZHANG Xinghua;LIU Hongyi(Electric Power Research Institute,CSG EHV Power Transmission Company,Guangzhou 510700,Guangdong,China)
出处
《沈阳工业大学学报》
北大核心
2026年第1期74-82,共9页
Journal of Shenyang University of Technology
基金
国家自然科学基金项目(U22B6008)
中国南方电网有限责任公司超高压输电公司科技项目(CGYKJXM20220111)。
关键词
无人机群
防空反制系统
部署优化
水波优化
A^(*)算法
路径规划
设施选址
进化算法
drone swarm
air defense countermeasure system
deployment optimization
water wave optimization
A^(*)algorithm
path planning
facility location
evolutionary algorithm
