Review of Nuclear Quadrupole Resonance Signal Processing and Analysis Methods

Abstract

This article presents a structured and comprehensive review of signal processing and analysis methods used in nuclear quadrupole resonance (NQR), with a focus on their performance under real-world operating conditions, where signals are typically weak, noisy, and highly sensitive to environmental influences. Classical approaches, including Fourier analysis, wavelet transforms, adaptive and matched filtering, as well as high-resolution spectral estimation techniques, are considered with respect to their ability to enhance signal detectability and frequency resolution. Particular attention is devoted to modern machine learning-based methods, including deep neural networks and hybrid architectures, which enable automatic feature extraction and demonstrate high robustness in low signal-to-noise ratio conditions. Each group of methods is systematically analyzed according to a set of practical criteria, including noise immunity, computational complexity, adaptability to nonstationary environments, sensitivity to parameter variations, and suitability for implementation in portable or resource-constrained NQR systems. The review highlights key challenges in current research, such as the lack of standardized and representative training datasets, limited interpretability of learning-based models, and difficulties associated with deploying computationally intensive algorithms in compact instrumentation. It is shown that no single signal processing approach provides a universally optimal solution across all operating scenarios. Instead, the most promising direction lies in the development of hybrid signal processing frameworks that combine classical preprocessing techniques with data-driven machine learning models. The presented analysis provides a methodological basis for the informed selection and design of robust, adaptive NQR systems intended for practical applications in security screening, material characterization, and chemical substance identification under diverse and challenging conditions.