基于改进遗传算法的协同干扰资源分配问题研究

doi:10.16182/j.issn1004731x.joss.25-FZ0648

摘要/Abstract

摘要：

针对协同干扰任务分配问题，提出一种基于改进遗传算法的协同干扰资源分配方法。采用基于熵权法的逼近理想解排序法(technique for order preference by similarity to an ideal solution，TOPSIS)对目标雷达在搜索模式与跟踪模式下进行威胁等级评估；分析影响干扰机干扰效果的因素，建立协同干扰效益评估模型，并构建以多部干扰机总干扰效益为目标函数、单部干扰机干扰能力为约束条件的干扰资源分配模型；从增加精英保留操作、使用自适应参数等方面改进遗传算法，并对干扰资源分配模型进行寻优解算。仿真结果表明：改进遗传算法相比标准遗传算法运行速度明显提高，且具有更高的寻优概率，能较好地解决协同干扰资源分配问题。

关键词: 雷达网, 干扰资源分配, 威胁等级评估, 干扰效益评估, 遗传算法

Abstract:

To address the cooperative interference allocation of jamming tasks, a cooperative interference allocation method of jamming resources was proposed based on the improved genetic algorithm. In search and tracking modes of the target radar, a threat level assessment was conducted by the technique for order preference by similarity to an ideal solution (TOPSIS) based on the entropy weight method. The factors affecting the jamming effectiveness of jammers were analyzed. A cooperative interference evaluation model of jamming effectiveness was established, and the allocation model of jamming resources was built with the total interference effectiveness of multiple jammers as the objective function and the interference ability of a single jammer as the constraint condition. The genetic algorithm was improved by incorporating elite preservation operations and applying self-adaptive parameters, and the allocation model of jamming resources was optimized. The simulation results have shown that the improved genetic algorithm has significantly enhanced the operating speed and optimization probability compared with the standard genetic algorithm, effectively solving the cooperative interference allocation of jamming resources.

Key words: radar network, allocation of jamming resource, threat level assessment, jamming effectiveness evaluation, genetic algorithm

中图分类号:

TP391.9

徐智霞,王蕊,孙楠等 . 基于改进遗传算法的协同干扰资源分配问题研究[J]. 系统仿真学报, 2025, 37(12): 3176-3189.

Xu Zhixia,Wang Rui,Sun Nan,et al . Research on Cooperative Interference Allocation of Jamming Resources Based on Improved Genetic Algorithm[J]. Journal of System Simulation, 2025, 37(12): 3176-3189.

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雷达	距离/km	高度/km	载频/GHz	重频/Hz	脉宽/μs
R₁	145	8.2	2.2	313	30
R₂	310	69	12	385	9
R₃	130	11.2	10	2 500	25
R₄	507	60	8	385	27
R₅	823	100	2	500	25
R₆	179	40	9.6	1 250	16
R₇	138	7.9	11	2 000	13
R₈	187	15	4.8	900	40

雷达工作模式	距离	高度	载频	重频	脉宽
搜索模式	0.16	0.19	0.36	0.20	0.09
跟踪模式	0.16	0.21	0.15	0.27	0.21

模式	R₁	R₂	R₃	R₄	R₅	R₆	R₇	R₈
搜索	0.92	0.27	0.47	0.45	0.58	0.42	0.43	0.78
跟踪	0.53	0.45	0.85	0.34	0.14	0.63	0.87	0.56

	J₁	J₂	J₃	J₄
R₁	0.8	0.6	0.2	0.5
R₂	0.5	0.6	0.2	0.9
R₃	0.6	0	0.7	0.4
R₄	0.4	0.4	0.6	0.8
R₅	0	0.4	0.8	0.7
R₆	0.4	0.8	0.9	0.3
R₇	0.7	0.5	0.5	0.6
R₈	0.5	0.7	0.6	0.5

模式	R₁	R₂	R₃	R₄	R₅
搜索	0.27	0.48	0.68	0.44	0.73
跟踪	0.16	0.61	0.48	0.66	0.56