一种基于微波雷达回波信号的车型分类方法

车辆类型识别方法是智能交通系统的关键技术之一。利用深度学习的高维特征泛化学习能力,将改进的LeNet-5卷积神经网络用于基于交通微波雷达的大小车型分类识别。首先,以雷达触发前的N帧信号为基础,对雷达的回波信号进行分析并构建数据集;然后,分析LeNet-5卷积神经网络的特点;最后提出一种改进的LeNet-5卷积神经网络。实验结果表明,与传统的支持向量机方法相比,所提方法能够智能学习大小车的雷达时频信号特征,大小车型识别准确率达到97%以上,可为交通场景下的车型识别研究提供新的技术途径。

A Vehicle Size Classification Method Based on Echo Signal of Microwave Radar

Vehicle type identification is one of the key technologies of intelligent transportation system.In this paper,the improved LeNet-5 convolutional neural network( CNN) is used to classify vehicles with different size based on traffic microwave radars by using the high-dimensional feature generalization learning ability of deep learning.Firstly,the radar echo signal is analyzed and the data set is constructed based on the previous N frame radar trigger signal. Then,the characteristics of LeNet-5 CNN are analyzed. Finally,an improved LeNet-5 CNN is proposed.Experimental results show that the proposed method can intelligently learn the radar time-frequency signal characteristics of the small vehicle compared with the traditional support vector machine method.The recognition accuracy rate of large and small models is more than 97%.It provides a new technical approach for vehicle identification research in traffic scenarios.