Efficient Performance Optimization for the Magnetic Data Readout From a Proton Precession Magnetometer With Low-Rank Constraint

Abstract

Proton precession magnetometer (PPM) is a potential device to measure the low-frequency geomagnetic field in the time domain. Nevertheless, the uncontrollable noise such as power frequency noise, random noise, etc., severely affects the detection results. In this paper, aimed to boost the performance of the PPM, a novel real-time processing algorithm for the optimization of PPM output strategy based on the low-rank constraint (LRC) is proposed, and a new efficient framework, dubbed LRC-based geomagnetic data readout optimizer (LRC-GDRO), is implemented. This method aims to extract the dominant principal components of the pure magnetic signal and identify the singular values of noise. Thereby, the relative ideal signal without external noise, which still contains the ambient field information is yielded. Moreover, we develop a prototype device and compare the test results before and after using the proposed method. Consequently, compared with traditional PPM, the performance of the PPM using the proposed method has been significantly improved with a measurement accuracy of ± 0.2 nT. Furthermore, the experimental results reveal that the LRC-optimized PPM is comparable to a very high-accuracy Overhauser magnetometer in terms of 12 h geomagnetic field observation.