车载超短波电台电波传播预测模型研究

doi:10.16180/j.cnki.issn1007-7820.2023.07.009

摘要/Abstract

摘要：

针对实战环境中车载超短波电台通信距离和质量受地面附着物和地形地貌影响的问题,文中基于射线追踪和机器学习,建立了车载超短波电台电波传播预测模型。采用装甲车辆与车载天线的一体化建模获得车载天线辐射方向图,融合电子地图,建立了基于射线追踪技术的电波传播仿真模型。利用随机森林机器学习算法和仿真模型的数据结果,建立了基于随机森林的电波传播预测模型,并与经典电波传播模型如Egli模型和Okumura-Hata模型进行对比。结果显示,基于随机森林的电波传播模型预测精度更高,均方根误差达到2.190 1 dB,决定系数达到0.960 1,可准确预测战术通信环境中的电波传播情况。

关键词: 超短波, 路径损耗, 射线追踪法, 电波传播模型, 机器学习, 随机森林, 电子地图, 车载天线

Abstract:

Given the problem that the communication distance and quality of the vehicle-mounted ultrashort wave radio are affected by ground attachments and topography in the actual combat environment, a radio wave propagation prediction model of vehicle-mounted ultrashort wave radio is established based on ray tracing and machine learning. The integrated modeling of armored combat vehicle and vehicle antenna is established to obtain the antenna radiation pattern, and combined with electronic images, the radio wave propagation simulation model based on ray tracing technology is established. Based on the machine learning algorithm of the random forest and data results for the simulation model, the radio wave propagation prediction model based on the random forest was established. Compared with traditional classical radio wave propagation models such as the Egli and Okumura-Hata models, the radio wave propagation prediction model based on the random forest has higher accuracy. The root mean square error reaches 2.190 1 dB, and the coefficient of determination reaches 0.960 1. It can accurately predict radio wave propagation in the tactical communication environment.

Key words: ultrashort wave, path loss, ray-tracing method, radio wave propagation model, machine learning, random forest, electronic images, vehicle antenna

中图分类号:

TN011

李敏,张光硕,徐至江,谢红星,路宏敏. 车载超短波电台电波传播预测模型研究[J]. 电子科技, 2023, 36(7): 64-69.

LI Min,ZHANG Guangshuo,XU Zhijiang,XIE Hongxing,LU Hongmin. Research on Radio Wave Propagation Prediction Model of Vehicle-Mounted Ultrashort Wave Radio[J]. Electronic Science and Technology, 2023, 36(7): 64-69.

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模型	参数名称	参数取值
随机森林	决策树个数	180
	内部节点分裂的最小样本数	2
	叶子节点的最小样本数	1
	叶子节点中样本的最小权重	0
	树的最大深度	10
k近邻	所选用的近邻数	3
	预测的权函数	权重是距离的倒数
	向量距离算法	欧氏距离
BP神经网络	隐藏层神经元个数	11

模型	RMSE/dB	R²
随机森林	2.190 1	0.960 1
k近邻	3.703 9	0.885 9
BP神经网络	5.927 5	0.707 8
线性回归	8.555 1	0.391 4
自由空间	15.266 8	-0.938 2
Egli	22.634 8	-3.260 4
Okumura-Hata	24.405 2	-3.952 9
ITU-R P.1546	25.742 2	-8.196 0