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Paper: Expert RF Feature Extraction to Win the Army RCO AI Signal Classification Challenge #472
referenced this pull request
May 23, 2019
In this paper, the authors presented a method to predict the radio signal modulation type and SNR, which won the Army RCO AI signal classification challenge. This paper is well written. The method is technically sound in general, and the results appear to be reproducible. The data source are clearly referenced.
Here are my questions/suggestions:
Releasing our entire codebase won't be possible. Instead I've tried to show which modules we relied upon like allantools and scipy.signal. I've also tried to make as clear as possible how individual features were calculated in latex. Moreover, since there really isn't an 'algorithm' at work here the code is really constrained to the individual features discussed. We may include code snippets on the poster that will be shown at the scipy 2019. Unfortunately getting code cleared for public release is quite difficult due to export control.
Ok no problem.
Ok; I can add this as a footnote.
This is addressed in our companion paper, available here as a separate submission. The DL network produced better results than the engineering features, but combined (ensemble) yielded even better results. Moreover the engineering features based network was very robust against test data that is unlike the training data. A pure DL network stumbled in this regard.
@yingweiy If this is OK, let me know and I'll make the two changes you suggest.
The paper is very well written and it is easy to follow. Signal processing is not my domain and I am not up to date with latest methodologies used in the field. However, the methodology is thorough and well explained. Even without releasing the code, I would definitely accept this paper.