Higher-Order Singular Value Tensor Decomposition-Based Tuning Frequency Estimation for FID Signals Under Low SNR
Wenjingping Zhang, Huan Liu, Haobin Dong, Zheng Liu, Xiangyun Hu · IEEE Geoscience and Remote Sensing Letters · 2024
The frequency of the free induction decay (FID) signal induced from an Overhauser magnetometer sensor is proportional to the magnetic field to be measured. Due to the low initial signal-to-noise ratio (SNR), sensor tuning is necessary to suppress the noise and improve the frequency estimation accuracy. To improve the tuning performance in complex strong-disturbance environments, this study introduces a novel method using higher-order singular value tensor decomposition (HOSVTD) and Fourier synchrosqueezing transform (FSST), namely HOSVTD-FSST. First, multiple FID signals are obtained using an equal delay multichannel acquisition strategy to establish a deeper, more intrinsic correlation attribute. Second, matrix segmentation is applied to construct the signals into a higher-order tensor for singular value computation, and the CANDECOMP/PARAFAC (CP) decomposition is fused to obtain a low-noise FID. Third, the FSST is employed to analyze the low-noise signal to extract the time-frequency ridges to capture the tuning frequency. Finally, the HOSVTD-FSST is compared with numerous commonly used methods. The experimental results demonstrate that under the presence of spike noise and with the SNR less than −20 dB, the frequency tuning deviations of the commonly used methods are up to 100 Hz, while that of the HOSVTD-FSST is within 5 Hz, which verifies that the HOSVTD-FSST can significantly enhance the sensor tuning accuracy in complex strong-disturbance conditions.