A novel wavelets method for cancelling time-varying interference in NQR signal detection

Interference cancelation is a very important aspect of Nuclear Quadrupole Resonance (NQR) signal detection, and can become really difficult when the interference is considerably time-varying. We propose a novel wavelets method to effectively remove (or reduce) time-varying interference in the data and facilitate a valid detection of the NQR signal. The proposed algorithm uses an extended Gabor-Morlet wavelets basis to approximate interference with complicated time-varying properties. The proposed algorithm utilizes our well designed cost function to extract the interference components out from NQR data strategically. Mathematical derivations and numerical results from both simulated and measured data demonstrate that the proposed algorithm can precisely cancel strong time-varying interference without distorting signal of interest improving NQR detection, even when interference and signal of interest are severely overlapped. The proposed algorithm is beyond normal wavelets methods such as standard wavelets denoising methods, and exhibits better performance than normal Fourier analysis and related frequency selective methods, and general adaptive filtering methods.