Detection of quasi-harmonic signals with a priori unknown parameters in strong additive noise by machine learning methods

Signals with a priori unknown parameters in strong noise are used in various fields of science and technology. This paper is devoted by features and limits deep neural networks for signal detection. We study quasi-harmonic signals with a priori unknown parameters. Neural network method was compared with classical methods for detecting signals in terms of accuracy and speed. We use realistic models of hexogen nuclear quadruple resonance (NQR) signals with parameters dependence by temperature. Experiments show that proposed method is more accurate and one hundred times faster than alternative ones. We achieve a probability of NQR signal detection about 95%, when signal-to-noise ratio is -15 dB and the signal parameters are unknown. When the signal-to-noise ratio is -20 dB, probability of NQR signal detection is 80%. (paper)