How can I classify two different types of radar modulation from images obtained in the time-frequency analysis?

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Hello, as in the example in "Radar Waveform Classification Using Deep Learning," I need to classify two forms of radar modulation under study. These waveforms do not fit into the FMCW, BARKER, RECT, FRANK, COSTAS and LFM image library. Note that the picture that I saved (.png) has different modulation between them and the conventional ones. I would like to classify them into two distinct categories, with an acceptable percentage, type A or B. In the example in "Radar Waveform Classification Using Deep Learning," I saw that it was possible to classify 3 different types of modulations (RECT LFM, BARKER), and I would like to do the same for these two waveforms that I am studying. The data matrix (image) of the time-frequency analysis that I am doing with STFT has 801 (bins frequency) x 1207 (time resolution). I did not use the Wigner-Ville due to the high computational cost, and as the modulations are very different, the time-frequency resolution of the STFT is sufficient. How could I succeed in this study? Could you help me please? I'm starting to study this part of Machine Learning, and I saw that this toolbox and your help are directly linked to my final goal. Thank you very much
image from modulation type 1
image from modulation type 2


Bjorn Gustavsson
Bjorn Gustavsson 2022 年 4 月 21 日
The second modulation is obviously a chirp. How can I be so sure? Well I cannot because there is no way I can distinguish a point-object accelerating away from the radar during one pulse and a stationary point-object scattering back a chirped radar-pulse (You haven't even put labels on the figures so there is no way anyone can say what they represent...). You can try to de-chirp those signals as best you can.
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