Vehicle classification based on convolutional networks applied to FM-CW radar signals

TL;DR

This paper explores using convolutional neural networks on transformed FM-CW radar signals to classify vehicles, demonstrating promising results in recognizing vehicle categories from radar data.

Contribution

It introduces a novel approach of transforming FM-CW radar signals into 3D inputs for CNNs to improve vehicle classification accuracy.

Findings

Good performance in vehicle category recognition

Effective transformation of radar signals for CNN input

Potential for improved radar-based vehicle classification

Abstract

This paper investigates the processing of Frequency Modulated-Continuos Wave (FM-CW) radar signals for vehicle classification. In the last years deep learning has gained interest in several scientific fields and signal processing is not one exception. In this work we address the recognition of the vehicle category using a Convolutional Neural Network (CNN) applied to range Doppler signature. The developed system first transforms the 1-dimensional signal into a 3-dimensional signal that is subsequently used as input to the CNN. When using the trained model to predict the vehicle category we obtain good performance.