This paper aims to explore the detection defect of residence times difference (RTD) fluxgate working in low-power mode and present the countermeasures for sensor resolution improvement and linearity enhancement. The main defects are amplitude and symmetry changes induced in the output spikes of fluxgate probe due to the magnetic field. These defects lead to thresholds deviation and asymmetry, then cause severe performance degradation especially on detection resolution and linearity according to the RTD theory. To overcome such effects, the optimized RTD method based on voltage extraction and feedback technology is proposed to implement magnetic field compensation and achieve a zero-field running regime of the RTD fluxgate. In this regard, the sensor linearity is improved by a factor of 38, and the resolution degradation effect is suppressed more than 6 times, verified by the laboratory experiments. The optimized detection method proposed in this paper demonstrated a great potential to achieve lower power consumption and will make the RTD fluxgate more promising technology among bio-magnetic applications.

Residence times difference (RTD) fluxgate magnetometers measure the magnetic field using the time difference between positive and negative pulses of the induced voltage signal in the time domain. Sensitivity is one of the most important parameters to evaluate the performance of the magnetometers. According to our best knowledge of existing literature, for the RTD fluxgate sensors, there are no available models that can clearly set up relationships between physical parameters of an RTD fluxgate and the sensor sensitivity. Moreover, the relationship between the sensitivity and the excitation conditions is very complicate, thus the sensitivity calibration process is time-consuming. This paper presents a sensitivity model at a near-zero magnetic field. According to the model, by using relevant parameters of the excitation coil and driving signal, and the hysteresis state of the magnetic core, accurate predictions of sensitivity for the sensors can be made. RTD fluxgates with different structures were designed and built to experimentally verify the presented sensitivity model. The testing results indicates that the model is valid and the relative error of its prediction on sensitivity is less than 4%. Using the proposed sensitivity model, we can not only study situations of various excitation signals but also have a theoretical foundation to miniaturize the RTD fluxgate sensors.

This study introduces a novel fluxgate current sensor with a compact, ring-shaped configuration that exhibits improved performance through the integration of magnetization residence times and neural networks. The sensor distinguishes itself with a unique magnetization profile, denoted as M waves, which emerge from the interaction between the target signal and ambient magnetic interference, effectively enhancing interference suppression. These M waves highlight the non-linear coupling between the magnetic field and magnetization residence times. Detection of these residence times is accomplished using full-wave rectification circuits and a Schmitt trigger, with a digital output provided by timing sequence detection. A dual-layer feedforward neural network deciphers the target signal, exploiting this non-linear relationship. The sensor achieves a linearity error of $0.054\%$ within a measurement range of 15 A. When juxtaposed with conventional sensors utilizing the residence-time difference strategy, our sensor reduces linearity error by more than 40-fold and extends the effective measurement range by $150\%$. Furthermore, it demonstrates a significant decrease in ambient magnetic interference.

Coupled-core fluxgate magnetometer (CCFM), which is based on the coupling-induced oscillations in bistable nonlinear dynamics, can achieve self-excitation and obtain superior performances compared with the single core fluxgate magnetometer under the premise of utilizing the same residence times difference readout method. One of the pivotal factors determining the performances of CCFM is the critical coupling factor. To get control of it and further enhance the sensor performances, the negative feedback is exploited and implemented by feeding the magnetization from induction coil to excitation coil of the same fluxgate element (self-feedback). Theoretical analyses, Simulink simulations with a quantitative model, and experimental results obtained in the magnetic shielded environment reveal the critical coupling factor adjustment effect of negative feedback on CCFM and confirm the substantial enhancement of sensor performances. Finally, the sensitivity of CCFM for the static magnetic field detection is improved from 0.0253 to 0.7895 μs/nT, and the magnetic resolution of CCFM is optimized from 0.395 to 0.049 nT. Therefore, the negative feedback is an effective and reliable method for performances enhancement of CCFM, and it has potential utilization on the oscillation systems with similar dynamic peculiarities for factor adjustment.

Recent theoretical works have shown that spontaneous oscillations can be induced by unidirectionally ring coupling the overdamped bistable nonlinear systems, subject to the proper initial states and coupling strength. These techniques have been adopted in the practical realization of the coupled core fluxgate magnetometer (CCFM) which obtains better magnetic field detection performances with residence times difference (RTD) readout strategy compared with the single core fluxgate magnetometer. In this paper, the effects of negative state feedback on CCFM have been investigated. Numerical simulations and preliminary experimental results, which have a good consistency, show a distinct benefit from the standpoint of sensitivity enhancement for CCFM utilizing negative state feedback.

Spin-exchange-relaxation-free (SERF) magnetometers, configurable for three-vector operation, have been deployed in many fields, such as magnetoencephalography (MEG), magnetocardiography (MCG), and precision tests of fundamental physics owing to their ultrahigh sensitivity and low size-weight-power (SWaP) characteristics. SERF magnetometers typically employ multiaxis magnetic field modulation to decouple the vector information. However, the interaxis crosstalk brought by intrinsic spin dynamics coupling reduces measurement accuracy, compromising source localization and inflating measurement error. To suppress this coupling, a strategy of a wide-band random signal employed as training inputs, paired with photodiode (PD) readings from the SERF magnetometer to construct a neural-network model, was utilized for the first time. This approach enabled the high-precision decoupling of the vector magnetic-field components by suppressing the maximum interaxis crosstalk from 10% to <3%. Beyond reducing crosstalk, the utilization of the wide-band random signal during field training enhanced the model's generalization capability across varying magnetic environments, offering a novel strategy for the construction of robust vector magnetometry.