基于遗传算法的相位调制波形智能优化

doi:10.12305/j.issn.1001-506X.2022.03.02

摘要/Abstract

摘要：

随着智能化程度的提高, 雷达发射信号更加复杂多变。为有效应对复杂的、未知的威胁信号, 需要提升对抗系统智能对抗的能力，提出一种基于智能优化算法的对抗波形智能优化方法, 并通过仿真实验对基于遗传算法的相位调制波形智能优化进行研究。不同优化参数、不同实施条件、不同雷达信号及信号变化条件下的实验结果表明, 遗传算法能够以较少的迭代次数和较短的运行时间对不同条件下的波形进行优化, 实现对抗性能的提升, 初步证明了该种方法的可行性。

关键词: 波形优化, 智能优化, 相位调制, 遗传算法

Abstract:

With the improvement of intelligence, the transmitting signal of radar becomes more complex and changeable. In order to effectively deal with complex and unknown threat signals, it is necessary to enhance the intelligent countermeasure capability of the countermeasure system. An intelligent optimization method of countermeasure waveform based on intelligent optimization algorithm is proposed. And the intelligent optimization of phase modulation waveform based on genetic algorithm is studied through simulation experiments. The experimental results under different optimization parameters, different implementation conditions, different radar signals and signal changes show that the genetic algorithm can optimize the waveform under different conditions with less iteration times and short running time, and realize the improvement of the countermeasures performance. The feasibility of this method is preliminarily proved.

Key words: waveform optimization, intelligent optimization, phase-modulation, genetic algorithm

中图分类号:

TN97

孙剑炜, 王超, 施庆展, 任文博, 尧泽昆, 袁乃昌. 基于遗传算法的相位调制波形智能优化[J]. 系统工程与电子技术, 2022, 44(3): 722-729.

Jianwei SUN, Chao WANG, Qingzhan SHI, Wenbo REN, Zekun YAO, Naichang YUAN. Intelligent optimization of phase-modulation waveform based on genetic algorithm[J]. Systems Engineering and Electronics, 2022, 44(3): 722-729.

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方法	目标函数/dB	目标函数计算次数	运行时间/s
穷举法	4.90	191	1.82
遗传算法	4.90	135	1.48

方法	目标函数/dB	目标函数计算次数	运行时间/s
穷举法	6.23	4 009	38.67
遗传算法	6.16	435	4.35

方法	目标函数/dB	目标函数计算次数	运行时间/s
穷举法	7.48	28 063	295.01
遗传算法	7.25	409	4.50

方法	目标函数/dB	目标函数计算次数	运行时间/s
穷举法	8.77	28 063	298.84
遗传算法	8.63	325	3.99

方法	参数名称	信号
方法	参数名称	1	2	3	4	5
穷举法	码元宽度/μs	1.50	2.67	0.58	0.50	0.53
	采样时长/μs	4.5	8.0	1.5	8.0	1.5
	转发次数	3	2	4	2	6
	目标函数/dB	7.48	6.42	2.57	1.58	4.66
	目标函数计算次数	28 063	28 063	28 063	28 063	28 063
	运行时间/s	297.99	734.11	292.18	1 363.22	292.15
遗传算法	最优初值/dB	4.77	3.52	-0.57	-3.07	1.44
	优化结果最小值/dB	6.89	5.86	2.35	-2.52	3.19
	优化结果最大值/dB	7.48	6.42	2.57	-0.44	4.66
	平均优化结果/dB	7.25	6.30	2.53	-1.55	4.07
	平均目标函数计算次数	409	470	367	316	438
	平均用时/s	4.56	12.89	3.99	16.66	4.90