Machine Learning For Modulation Classification Of Radar Signals: A Survey

Automatic modulation recognition of radar waveform is a major topic and has many military applications. This paper surveys the models and the techniques used in recognizing different modulation types of intercepted radar waveform. The literature shows the outstanding performance of deep learning neural network at low SNR values and in signal overlapped environments as well. Additionally, using different mathematical and statistical algorithms demonstrated that utilized in features extraction of the data in order to feed them into the neural network improves the performance significantly. However, reducing computation complexity is in development too.