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That's if they aren't already.

Without having the data I can't try but I'd guess that Fourier analysis (or something a bit more clever to deal with noise e.g. Welch) would get you a lot of the way there even without invoking AI

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This is not based on any classified information and sonar signal processing is not something i know much about, but cyclostationarity (and other higher-order statistical signal processing) does tend to be useful when processing signals that are generated by processes where there's multiple periodicities at work: https://en.wikipedia.org/wiki/Cyclostationary_process#Angle-...

Signal separation techniques for characterizing cyclostationary signals are also more robust to noise. For example, analysis of the second order FFT can resolve the signals of incipient faults in rolling bearings because it can resolve the signals of the bearing components rotating (at a much lower frequency than the shaft) whereas simple FFT analysis would generally only measure the magnitude of the structural resonance of the machine, with spikes at the mains, rotational, and slip frequency.

Even if you're also using ML, Fourier transforms can be pretty useful in this sort of analysis.

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