Study of Chopping Magnetic Flux Modulation on Surface Acoustic Wave Magnetic Sensor

ICECS 2022(2022)

引用 0|浏览7
暂无评分
摘要
Some methods of magnetic flux modulation are used to overcome flicker phase noise, low-frequency acoustical distortions, and movement artifacts. This work proposes employing a chopping flux modulation technique controlling a high permeability toroid together with a surface acoustic wave sensor inside. In this primary proof-of-concept study, an external magnetic field is generated to estimate quantitative signal parameters and the effect of the toroid shielding factor. Finally, the limitations of this approach should be identified and how low-frequency magnetic signals are influenced. The achievable sensitivity was empirically evaluated, and a quantitative signal quality value was calculated by estimating the signal power spectrum and noise power spectrum. Thus, the study compares the signal-plus-noise to noise ratio with and without magnetic flux modulation of a reproducible excitation magnetic signal generated by a solenoid coil. The experimental results show that the noise floor of this magnetic sensor system is improved. However, the signal-plus-noise to noise ratio without the modulation is 17dB, and with the modulation, this parameter becomes 13dB for a given mono-frequency signal of 20 mu T. In perspective, this method exhibits disadvantages in reducing the sensitivity because, with the toroid inside, the calibration factor of the solenoid is not the same anymore, and the shielding factor reduces the field strength of the alternative-current field. Furthermore, the results show that the chopping flux modulation technique requires exploring how to compensate for the losses and setup issues that affect the magnetic field to define how suitable it is for surface acoustic waves magnetic sensors.
更多
查看译文
关键词
Surface-acoustic-wave, magnetic sensors, chopping flux modulation, flicker phase noise, magnetic shielding, signal-plus-noise to noise ratio
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要