Magnetic modulation methods especially Micro-Electro-Mechanical System (MEMS) modulation can improve the sensitivity of magnetoresistive (MR) sensors dramatically, and pT level detection of Direct Current (DC) magnetic field can be realized. While in a Low Frequency Alternate Current (LFAC) magnetic field measurement situation, frequency measurement is limited by a serious spectrum aliasing problem caused by the remanence in sensors and geomagnetic field, leading to target information loss because frequency indicates the magnetic target characteristics. In this paper, a compensation field produced with integrated coils is applied to the MR sensor to remove DC magnetic field distortion, and a LFAC magnetic field frequency estimation algorithm is proposed based on a search of the database, which is derived from the numerical model revealing the relationship of the LFAC frequency and determination factor [defined by the ratio of Discrete Fourier Transform (DFT) coefficients]. In this algorithm, an inverse modulation of sensor signals is performed to detect jumping-off point of LFAC in the time domain; this step is exploited to determine sampling points to be processed. A determination factor is calculated and taken into database to figure out frequency with a binary search algorithm. Experimental results demonstrate that the frequency measurement resolution of the LFAC magnetic field is improved from 12.2 Hz to 0.8 Hz by the presented method, which, within the signal band of a magnetic anomaly (0.04-2 Hz), indicates that the proposed method may expand the applications of magnetoresistive (MR) sensors to human healthcare and magnetic anomaly detection (MAD).

In general, giant magnetoresistance (GMR) sensors are only sensitive to the magnetic field in the plane of the substrate due to fabrication restraints. Then, a three-axis magnetic sensor based on GMR sensors has to be assembled to obtain three magnetic field components. Novel magnetic flux guides for three-axis magnetic sensor are described in this paper. These novel flux guides are designed with flux conversion structure and are able to bring magnetic flux out-of-plane to in-plane. With these flux guides, the magnetic field perpendicular to the chip surface can be detected with the GMR sensors in-plane. Then, numerical simulations are performed to validate the designs, and optimizations are made to improve the flux guides' performance. Based on these works, a three-axis magnetic sensor based on the in-plane GMR sensors is proposed. Without assembling the z-axis sensor, the integrated three-axis magnetic sensor is expected to have better angular position. According to the designs, the flux guides can be deposited with good symmetry. Therefore, Wheatstone bridges are configured to deduce a differential voltage, only relative to a certain component of the magnetic field. In addition, the magnetic field at the active region of the GMR sensor would be intensified. In addition, the sensitivity of the sensor can be improved due to the amplification ability of the flux guides.

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Frequency estimation is a fundamental problem in many applications, such as traditional vibration measurement, power system supervision, and microelectromechanical system sensors control. In this paper, a fast and accurate frequency estimation algorithm is proposed to deal with low efficiency problem in traditional methods. The proposed algorithm consists of coarse and fine frequency estimation steps, and we demonstrate that it is more efficient than conventional searching methods to achieve coarse frequency estimation (location peak of FFT amplitude) by applying modified zero-crossing technique. Thus, the proposed estimation algorithm requires less hardware and software sources and can achieve even higher efficiency when the experimental data increase. Experimental results with modulated magnetic signal show that the root mean square error of frequency estimation is below 0.032 Hz with the proposed algorithm, which has lower computational complexity and better global performance than conventional frequency estimation methods.