Vehicle classification based on convolutional networks applied to FM-CW radar signals
This work addresses vehicle classification for radar signal processing, but it is incremental as it applies an existing deep learning method to a specific domain.
The paper tackles vehicle classification by applying a convolutional neural network to FM-CW radar signals, achieving good performance in recognizing vehicle categories.
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.