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为提高无人机集群执行搜索任务时的射频隐身性能,提出了一种基于射频隐身的无人机集群搜索任务分配算法。该算法旨在通过任务优化分配,最小化无人机集群截获概率的同时,最大化无人机集群的探测概率。首先,推导了无人机集群雷达探测概率和截获概率的表达式,并以此作为双目标优化函数,以完成任务的消耗时间为约束条件,建立双目标优化模型。然后,采用标准粒子群优化算法对该模型求解,求得其帕累托集。仿真结果表明:与随机任务分配算法和最小任务完成时间分配算法相比,所提算法可在搜索性能几乎没有损失的情况下,有效提升无人机集群执行搜索任务时的射频隐身性能。
Abstract：In order to improve the radio frequency stealth( RFS) for UAV swarm when performing search tasks,a searching task allocation algorithm for UAV swarm is developed. The purpose of the proposed algorithm is to minimize the intercepted probability of UAV swarm signal and meanwhile maximize the detection probability of UAV swarm radar by task allocation. First,the detection probability and intercepted probability of UAV swarm are derived as objective functions,and the algorithm is formulated as a Pareto bi-objective optimization model with the constraint of total consume time. Then,a technique which exploits standard particle swarm optimization algorithm( SPSO) is adopted to obtain the Pareto set of the underlying bi-objective optimization problem. The simulation results demonstrate that the presented scheme can provide superiority over random tasks allocation( R-TA) scheme and minimum consuming time task allocation( MCT-TA) scheme in terms of the achievable RFS performance,with almost no loss on detection ability.
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Basic Information:
DOI：10.16358/j.issn.1009-1300.2020.1.118
China Classification Code:V279;V218
Citation Information:
[1]张巍巍,时晨光,周建江.基于射频隐身的无人机集群搜索任务分配算法[J].战术导弹技术,2020,No.204(06):60-66+119.DOI:10.16358/j.issn.1009-1300.2020.1.118.
Fund Information:
国家自然科学基金青年基金项目(61801212); 中国博士后科学基金面上项目(2019M650113); 江苏省自然科学基金青年基金项目(BK20180423); 装备预研重点实验室基金(6142401200402); 中央高校基本科研业务费(NT2019010)