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2.1901 dB,决定系数达到0.9601,可准确预测战术通信环境中的电波传播情况。
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 2.1901 dB,and the coefficient of determination reaches 0.9601.It can accurately predict radio wave propagation in the tactical communication environment.
作者
李敏
张光硕
徐至江
谢红星
路宏敏
LI Min;ZHANG Guangshuo;XU Zhijiang;XIE Hongxing;LU Hongmin(School of Electronic Engineering,Xidian University,Xi′an 710071,China)
出处
《电子科技》
2023年第7期64-69,共6页
Electronic Science and Technology
基金
国防预研项目(JZX7X201901JY0048)。
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
ultrashort wave
path loss
ray-tracing method
radio wave propagation model
machine learning
random forest
electronic images
vehicle antenna
