CNN Based Target Classification in Vehicular Networks with Millimeter-Wave Radar

Abstract

Millimeter-wave radar is widely used in intelligent vehicle infrastructure cooperative systems (IVICS) because of its long detection distance, high integration and less affected by weather conditions. The target recognition and classification technology based on millimeter wave radar is a research hotspot. However, traditional recognition methods require a complex feature extraction process. In this paper, a millimeter-wave radar classification method based on deep learning is proposed, which uses the ability of convolutional neural networks (CNN) method to automatically extract feature data, so as to replace most of the complex processes of traditional radar signal processing chain. It solves the four classification tasks of bus, cars, electromobile and pedestrians. Taking the selected feature data as the RV spectrum, a seven layer convolution neural network is built to classify the target. Compared with traditional machine learning methods, CNN greatly reduces the workload of feature engineering, and its classification effect is better than traditional machine learning methods. Then, from the perspective of redundancy of the convolution kernels, we simplify the CNN architecture. As a result, final classification accuracy of the CNN is 93.16%. After simplifying the structure of the CNN, the computational complexity and storage consumption of the network are reduced by 74.67% and 73.71%, respectively, while ensuring that the classification accuracy does not decline.