Abstract
The use of convolutional neural networks (CNN’s) for radar detection is evaluated. The detector includes a time-frequency block that has been implemented by the Wigner-Ville distribution and the Short-Time Fourier Transform to test the suitability of both techniques. The CNN detectors are compared with the classic multilayer perceptron and with several traditional non-neural detectors. Preliminary results are shown using non-correlated and correlated Rayleigh-envelope clutter.
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López- Risueño, G., Grajal, J., Haykin, S., Díaz-Oliver, R. (2002). Convolutional Neural Networks for Radar Detection. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_186
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DOI: https://doi.org/10.1007/3-540-46084-5_186
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