Abstract
Nuclear quadruple resonance (NQR) has excellent potential for the remote detection of nitrogen-containing substances, such as trinitrotoluene (TNT). However, using NQR techniques in security systems have some problems. For example, unknown temperature of the detecting explosives and low signal-to-noise ratio (SNR) leads to a priori uncertainty of the parameters of the NQR signal. In the article, we use machine learning methods for detecting the NQR signal. It allows us to increase speed and accuracy of TNT NQR signal detection. We have shown that proposed method of NQR signal detecting is more accurate and 100 times faster than alternative methods if temperature uncertainty is above 10 degrees. We achieve probability of NQR signal detection about 95% for SNR -15 dB.
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