基于自适应变邻域搜索的火控雷达组网资源调度研究  

Research on resource scheduling of fire control radar networking based onadaptive variable neighborhood search

作  者:汪达旺 陆志沣 伍国华 WANG Dawang;LU Zhifeng;WU Guohua(School of Automation Electro-Mechanical Engineering Institute,Central South University,Changsha 410075,China;Shanghai Electro-Mechanical Engineering Institute,Shanghai 201109,China)

机构地区:[1]中南大学自动化学院,湖南长沙410075 [2]上海机电工程研究所,上海201109

出  处:《系统工程与电子技术》2025年第2期496-507,共12页Systems Engineering and Electronics

基  金:国家自然科学基金(62373380)资助课题。

摘  要:在海上舰艇编队防空作战中,传统火控雷达由于实行单目标对单雷达跟踪策略,难以满足复杂的现代作战需求。针对该问题,提出一种基于自适应变邻域搜索(adaptive variable neighborhood search, AVNS)的火控雷达组网资源调度方法。以海上舰艇编队火控雷达资源为基础,考虑雷达剩余通道数、雷达探测范围和预测航迹跟踪覆盖率约束,构建以资源目标距离、有效航迹覆盖率和目标威胁度为目标函数的资源调度模型,设计基于AVNS的组网策略算法。在设置的两种海上舰艇编队模拟队形场景中,与传统规则算法和变邻域搜索(variable neighborhood search, VNS)算法对比,验证所提方法的合理性和有效性。In naval warship formation air defense combats,traditional fire control radars,due to its implemen-tation of a single target to single radar tracking strategy,are difficult to meet complex modern combat needs.To address this issue,a fire control radar networking resource scheduling method based on adaptive variable neighborhood search(AVNS)is proposed.Based on the fire control radar resources of naval warship forma-tions,considering the constraints of remaining radar channels,radar detection range,and predicted trajectory tracking coverage rate,a resource scheduling model is constructed with the objective functions of resource target distance,effective trajectory coverage rate,and target threat degree.A networking strategy algorithm based on AVNS is designed.The rationality and effectiveness of the proposed method is verified by comparing it with traditional rule algorithms and variable neighborhood search(VNS)algorithms in two naval warship formation simulated formation scenarios.

关 键 词:海上防空 资源调度 自适应变邻域搜索 雷达组网 火控雷达 

分 类 号:E955[军事—军事工程]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象