Research on a secondary tuning algorithm based on SVD & STFT for FID signal. (2nd September 2016)

• Record Type: Journal Article
• Authors: Liu, Huan; Dong, Haobin; Ge, Jian; Bai, Bingjie; Yuan, Zhiwen; Zhao, Zhizhuo
• Is Part Of: Measurement science & technology. Volume 27:Number 10(2016:Oct.)
• Publication Date: 2016-09-02
• Subjects: secondary tuning algorithm -- SVD -- STFT -- FID signal -- proton precession magnetometer; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/0957-0233/27/10/105006
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: The tuning precision of a Proton precession magnetometer's sensor is the key to getting the best signal to noise ratio (SNR) of free induction decay (FID) signals. By analyzing the noises of the magnetometer's sensor and conditioning circuit, this paper introduces the principle of tuning and proposes a secondary tuning algorithm based on the singular value decomposition (SVD) and short-time Fourier transform (STFT), targeting the current lack of a tuning method. Moreover, the STFT for an FID signal's feature analysis is applied for the first time. First, the space matrix is constructed by the acquisition of ADC for the untuned FID signal, and then the SVD is performed to eliminate the noise and obtain the useful signal. Finally, the STFT technique is applied to the denoised signal to extract the time-frequency feature. By theory analysis, simulation modeling and the testing of an actual FID signal, the results show that, compared with general tuning methods such as peak detection and fast Fourier transform (FFT), the proposed algorithm improves the sensor's tuning precision, and the time of the tuning process is no more than one second. Furthermore, the problem of mistuning in strong-disturbance environments is solved. Thus, the secondary tuning algorithm based on the SVD and STFT is more practical.