Abstract
This paper investigates the processing of Frequency-Modulated Continuous-Wave (FMCW) 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 signatures. 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 obtained good performance.
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Acknowledgements
The authors wish to tank Infomobility S.R.L. Concordia sulla Secchia (Modena, Italy) and Autostrade per l’Italia (Roma, Italy) for having provided the radar data.
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Capobianco, S., Facheris, L., Cuccoli, F., Marinai, S. (2018). Vehicle Classification Based on Convolutional Networks Applied to FMCW Radar Signals. In: Leuzzi, F., Ferilli, S. (eds) Traffic Mining Applied to Police Activities. TRAP 2017. Advances in Intelligent Systems and Computing, vol 728. Springer, Cham. https://doi.org/10.1007/978-3-319-75608-0_9
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DOI: https://doi.org/10.1007/978-3-319-75608-0_9
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