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.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All the articles published in this issue are licensed under the terms of the Creative Commons Attribution Non-Commercial License (CC Attribution 4.0 License. (http://creativecommons.org/licenses/by-nc/4.0/)) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.