基于模式/区域分解的无人机集群电磁特性快速分析

doi:10.12305/j.issn.1001-506X.2021.10.01

摘要/Abstract

摘要：

无人机(unmanned aerial vehicle, UAV)集群在军事和民用领域的应用越来越广泛, 针对UAV集群目标电磁散射特性的获取和分析对生成对抗策略及反UAV有着重要的研究价值。目前, UAV集群动态测量数据获取难度较大、成本高; 而对于仿真数据, 传统计算电磁方法建模精度和效率较低, 有效的特性数据不足。因此, 提出了一种基于模式/区域分解的UAV集群目标电磁仿真方法, 该方法在矩量法的基础上, 引入特征模分解和区域分解技术, 前者极大地降低了计算未知量, 后者有效地解决了建模精度问题, 为UAV集群目标特性数据的生成提供了创新技术支撑。

关键词: 无人机集群, 电磁散射特性, 模式分解, 区域分解

Abstract:

Unmanned aerial vehicle (UAV) groups have been widely used in the modern society and military. The acquisition and analysis of the electromagnetic scattering characteristics of UAV group targets has important research value for anti-UAV. At present, the valid scattering data of UAV groups is insufficient because the acquisition of dynamic measurement data is difficult and costly. And for the simulation data, traditional computational electromagnetic methods lack modeling accuracy and efficiency. Therefore, a electromagnetic simulation method of UAV cluster target based on the mode and domain decomposition is proposed to solve this problem. This method reduces the number of unknowns greatly by using characteristic modes. and increases the modeling accuracy by introducing the eigenmode decomposition and the domain decomposition method. It provides a new technical support for the generation of electromagnetic scattering data of UAV groups.

Key words: unmanned aerial vehicle (UAV) group, electromagnetic scattering characteristics, mode decomposition, domain decomposition

中图分类号:

管灵, 朱晨曦, 董纯柱, 殷红成. 基于模式/区域分解的无人机集群电磁特性快速分析[J]. 系统工程与电子技术, 2021, 43(10): 2697-2705.

Ling GUAN, Chenxi ZHU, Chunzhu DONG, Hongcheng YIN. Fast analysis of electromagnetic characteristics for UAV groups based on mode and domain decomposition[J]. Systems Engineering and Electronics, 2021, 43(10): 2697-2705.

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模式数	单机/%	集群/%
1	99.9	99.9
5	64.1	69.2
10	17.9	21.9
15	3.1	5.3
20	2.8	4.9

对比项		方法
对比项		FEKO	CM
未知量		367 800	400
峰值内存/GB		29.3	2.5
时间	模式分解/h	—	0.1
	矩阵填充/h	4.1	4.1
	矩阵降阶/h	—	0.4
	矩阵求解	17.1 h	4.1 s
总时间/h		21.2	4.6

对比项		方法
对比项		MLFMA	MLFMA-CM
未知量		410 670	2 850
峰值内存/GB		41.2	1.3
时间	模式分解/h	—	0.3
	矩阵填充/h	5.3	5.4
	矩阵降阶/h	—	2.1
	矩阵求解	21.4 h	27.8 s
总时间/h		26.7	7.8

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