A Novel SSA-NLLSF Approach for Denoising NQR Signals

Nuclear quadrupole resonance (NQR) spectroscopy is used to identify narcotics and explosive materials. Detection of NQR signal generated by $${14}^{{N}}$$ isotope in an open environment is a challenging task due to the presence of external random noise and RF interference. Unlike the existing wavelet-based and other frequency-domain approaches that use averaged data, the present work exploits raw data by saving the acquisition time. In this context, a novel singular spectral analysis (SSA) and non-linear least square fit (NLLSF) algorithms are proposed to denoise the NQR signal and obtain the required parameters for detection of the NQR signal. Considering signal to noise ratio (SNR), segmental SNR (SSNR), and Pink noise as the performance parameters, the proposed algorithms are tested under various noise conditions by passing synthesized NQR signal and observed 35.7 dB gain SSNR. Furthermore, tests are carried out on NQR spectroscopy data acquired from the $$\hbox {NaNO}_{2}$$ sample, and an improvement in terms of signal quality and acquisition time are noticed.