空射诱饵弹干扰资源动态分配策略

doi:10.12382/bgxb.2022.0032

摘要/Abstract

摘要：

基于空射诱饵弹干扰机理和战术使用原则,针对诱饵弹动态干扰资源分配中存在的问题,提出一种更加贴合战场实际且时效性更强的空射诱饵弹对抗组网雷达干扰资源动态分配方法。采用基于ICRITIC—变权理论的威胁评估方法确定组网雷达的动态威胁矩阵;结合诱饵弹有源假目标干扰模式,从匹配度角度分析了影响干扰效果的相关指标,并构造了干扰效能矩阵;基于最优干扰效能建立了资源优化目标模型,并利用改进粒子群优化算法对资源优化问题进行求解。仿真结果表明,提出的优化方法能更合理地实现干扰资源分配,更加符合诱饵弹作战实际;相比传统方法,其计算效率和最优解正确率具有更明显优势。

关键词: 微型空射诱饵, 动态分配, 变权理论, 威胁评估, 干扰效能评估, 改进粒子群优化算法

Abstract:

In order to address the problems existing in dynamic jamming resource allocation, based on the jamming mechanism and tactical principles of miniature air-launched decoy (MALD), a dynamic allocation method of jamming resources for MALD to counter the netted radar system, which is more relevant to the battlefield and has better timeliness, is proposed. Firstly, a threat assessment method based on the ICRITIC variable weights theory is used to determine the dynamic threat matrix of the netted radars. Then, combined with the MALD’s active decoy jamming mode and based on the matching degree, the relevant indexes affecting the jamming effect are analyzed, and the jamming effectiveness matrix is constructed. Finally, the objective model of resource optimization is established based on the optimal jamming efficiency, and the improved particle swarm optimization algorithm is adopted to solve the resource optimization problem. The simulation results show that the proposed optimization method can achieve jamming resource allocation in a more rational way and better conforms to the actural operational situation of MALD. Compared with the traditional methods, the computational efficiency and the accuracy of the optimal solution have more remarkable advantages.

Key words: miniature air-launched decoy, dynamic allocation, variable weights theory, threat assessment, jamming effectiveness assessment, improved particle swarm optimization algorithm

陈美杉, 刘赢, 曾维贵, 钱坤. 空射诱饵弹干扰资源动态分配策略[J]. 兵工学报, 2023, 44(5): 1443-1455.

CHEN Meishan, LIU Ying, ZENG Weigui, QIAN Kun. Dynamic Jamming Resource Allocation Strategy of MALD[J]. Acta Armamentarii, 2023, 44(5): 1443-1455.

图/表 14

图1 空射有源诱饵原理结构示意图

Fig.1 Schematic diagram of the principle and structure of MALD

图2 多目标突防组网雷达系统二维平面图

Fig.2 2D plane graph of multiple targets penetrating the NRS

图3 干扰资源分配方案生成框架

Fig.3 Frame diagram of jamming resource allocation scheme generation

图4 改进PSO算法流程图

Fig.4 IPSO algorithm flowchart

表1 初始时刻雷达仿真参数

Table 1 Radar simulation parameters at the initial time

雷达名称	地面坐标/km	载频/MHz	脉宽/μs	重频/Hz	发射功率/dBm	增益/dB	探测时间/s
R1,S波段搜索雷达	(40,20,0)	3260	1.8	790	93.4	36	20
R2,C波段搜索雷达	(38,18,0)	5869	2.2	650	93.4	38	32
R3,C波段制导雷达	(38,24,0)	5298	1.2	1680	93.0	36	36
R4,X波段火控雷达	(30,27,0)	8700	1.5	2180	92.6	40	29
R5,X波段跟踪雷达	(40,30,0)	9860	1.3	2650	93.0	36	35
R6,S波段搜索雷达	(30,34,0)	2470	2.0	860	92.6	42	30

表2 初始时刻诱饵弹仿真参数

Table 2 MALD simulation parameters at the initial time

诱饵弹编号	空间起始坐标/km	增益/dB	发射功率/dBm	干扰样式种类	干扰工作时间/s	干扰延迟时间/μs
1	(14,16.5,0.7)	15	59.0	6	15	0.8
2	(14.5,15.8,0.8)	15	57.8	5	25	1.6
3	(15,14,0.9)	15	61.7	4	20	1.0
4	(15.5,14,1)	15	57.0	4	13	1.2
5	(15.7,16.2,1.1)	15	56.0	5	28	1.0
6	(15,15,1.2)	15	60.0	3	30	0.4
7	(16.8,15.3,1.3)	15	58.8	6	28	0.6
8	(16.1,15.1,1.4)	15	59.8	8	17	1.5

图5 目标突防组网雷达系统空间关系图

Fig.5 Scenario of target penetrating the NRS

图6 雷达指标权重变化曲线

Fig.6 Curves of radar index weight change

图7 各雷达威胁值变化曲线

Fig.7 Curves of the threat value of each radar

表3 优化算法资源分配结果

Table 3 Results of resource allocation achieved by IPSO

诱饵弹编号	时刻/s
诱饵弹编号	1~16	17~22	23~30
1	5	5	5
2	1	2	1
3	5	5	5
4	6	6	4
5	3	1	6
6	2	4	2
7	3	3	3
8	4	4	4

图8 最优干扰效能变化曲线

Fig.8 Curves of optimal jamming effectiveness change

表4 算法耗时性能对比

Table 4 Performance comparison between algorithms

时刻/s	分配耗时/s			干扰效能值
时刻/s	改进PSO算法	PSO算法	ACA	干扰效能值
1	0.1534	0.1105	0.1364	1.3313
5	0.0646	0.0839	0.1247	1.3532
10	0.0735	0.0922	0.1210	1.3815
15	0.0569	0.0858	0.1187	1.4583
20	0.0721	0.1021	0.1203	1.4920
25	0.0735	0.0882	0.1112	1.5065
30	0.0762	0.0836	0.1029	1.5591

图9 算法迭代曲线对比

Fig.9 Comparison of algorithm iteration curves

图10 最优解正确率对比分析

Fig.10 Comparison of accuracy of optimal solution

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