摘要
不断增多的车载通信设备数量导致车载通信系统面临日益严重的电磁干扰问题。针对复杂电磁环境下车载通信设备性能评估问题,文中基于神经网络非线性拟合精度高及自调节功能强的特点,提出了一种车载通信设备性能评估方法。依据车载通信设备的关键技术指标,建立了发射、传输、接收的链路评估体系,构建了基于BP神经网络的车载通信设备性能评估模型。利用MTALAB使用大量数据样本优化训练BP神经网络模型结构,提高了评估模型精度。验证结果表明,所构建神经网络评估模型归一化均方误差可达-36 dB,且评估误差较小。
With an increasing number of vehicular communication equipment,the communication system is faced with increasingly serious electromagnetic interference problem.In this study,based on the characteristics of high nonlinear fitting accuracy and strong self-tuning of BP neural network,an evaluation method is proposed to evaluate the performance of vehicular communication equipment under the complex electromagnetic environment.According to the critical technology indicators of vehicular communication equipment,an evaluation system including transmission,reception and interaction is established,and an evaluation model based on BP neural network structure is constructed.With the use of MTALAB software,large amounts of sample data are adopted to train and optimize the BP neural network model structure,and to improve the evaluating model accuracy.The validation results indicate that the normalized mean square error of the model reaches-36 dB,and the evaluation error is small.
作者
孟晓姣
张世巍
李小健
李敏玥
宋丙鑫
路宏敏
MENG Xiaojiao;ZHANG Shiwei;LI Xiaojian;LI Minyue;SONG Bingxin;LU Hongmin(School of Electronic Engineering,Xidian University,Xi’an 710071,China;EMC Laboratory,China North Vehicle Research Institute,Beijing 100072,China)
出处
《电子科技》
2021年第5期24-28,共5页
Electronic Science and Technology
基金
国防预研项目(JZX7X201901JY0048)。
关键词
车载通信设备
通信性能
BP神经网络
通信距离
评估模型
模型训练
NMSE
评估精度
vehicle-mounted communication equipment
communication performance
BP neural network
communication distance
evaluation model
model training
NMSE
evaluation accuracy
