ABSTRACT
Magnetic anomaly detection technologies have been widely used for tracking moving targets. In this paper, we present a fast-tracking method for magnetic abnormalities using a distributed Overhauser magnetometer system based on the genetic algorithm. Our proposed framework of the Overhauser magnetometer system employs multiple sensors to eliminate background interference, and the genetic algorithm efficiently solves magnetic anomaly data without requiring the derivation of the objective function. Test platforms were built to evaluate the distributed Overhauser magnetometer system and the genetic algorithm. Results from the natural outdoor magnetism laboratories showed that the noise of our presented magnetometers was below 0.134 nT. The optimal factors for solution precision and effectiveness in the genetic algorithm were obtained from the simulation. Moreover, the outdoor tracking experiments indicated that the proposed method could accurately and quickly detect the moving ferromagnetic object within 6.9% maximum positioning error in 0.55 m, and the tracking precision of the object velocity can get 5.88% maximum error in 4.33 km/h.
ABSTRACT
An Overhauser geomagnetic sensor is a precise instrument commonly employed for geomagnetic field observation, magnetic surveys, and so on. Currently, the miniaturization of the Overhauser geomagnetic sensor is limited due to the lower signal-to-noise ratio. Thus, how to effectively extract weaker free induction decay (FID) signal from a miniaturized sensor and how to improve the signal quality have become the bottleneck. To address these problems, we came up with an optimal design of the FID signal sensing coil for a miniaturized Overhauser geomagnetic sensor and propose a front-end matching circuit for the sensing coil to inhibit the attenuation of the signal amplitude caused by high impedance, further reducing the overall noise floor of the signal acquisition system. Finally, the field experimental results show that the miniaturized prototype sensor has a smaller volume and mass with an approximate performance compared with the commercial sensor.
ABSTRACT
In this Note, a new compressed sensing-based tuning algorithm has been developed to boost the sensor tuning performance of the proton precession magnetometers (PPMs). An end-to-end framework for the PPM's sensing free induction decay (FID) signal resonance based on orthogonal matching pursuit compressed sensing (OMPCS), dubbed OMPCS-FID resonance (OMPCS-FIDR), is developed and its working principle and implemented strategy are elaborated. By comparing the new sensor tuning approach with the state-of-the-art algorithms, i.e., peak detection, auto-correction, and secondary tuning, the results demonstrate that the proposed tuning method not only retains the performance but also overcomes the drawbacks of the state-of-the-art methods, which accelerates the possibilities of the PPM working in a scenario with a strong gradient magnetic field.
ABSTRACT
The proton precession magnetometer (PPM) is a commonly used device to measure the varying magnetic field. Since the frequency of the PPM sensing free induction decay (FID) signal is proportional to the magnetic field, the signal-to-noise ratio (SNR) is always a critical issue that influences the measurement accuracy severely due to the external interferences such as harmonic noise and random noise. In this study, to boost the SNR of the FID signal, an effective filtering algorithm based on time-frequency peak filtering (TFPF) analyzed with pseudo-Wigner-Ville distribution (PWVD) is proposed. Through pre-treating the collected noisy FID signal with frequency modulation and instantaneous frequency estimation using the peak value of the time-frequency characterization, the embedded noise can be decorrelated and the relative pure FID signal can be detected regardless of the impact of varying noise levels. The superiority of the proposed synaptic noise reduction framework, namely, TFPF-PWVD, was found by comparing it with state-of-the-art approaches under the same conditions. The results illustrated that even though in a strong-noisy scenario, the proposed TFPF-PWVD based approach still achieved the best SNR for the yielded sensing FID and the minimum standard deviation for the observed magnetic field data, which can enhance the geomagnetic measuring performance of a PPM.