Jian Ge

The data quality of an aeromagnetic survey is determined by the compensation for the magnetic interference of the aircraft. In existing neural-network-based magnetic compensation, the dimensionality of the input parameters of the magnetic interference network is high, and the determination of the input depends on personal experience and preliminary experiments, increasing the probability of overfitting and compensation instability. In addition, the interference features vary with time, while the existing models do not have temporal memory, resulting in poor generalization of the interference prediction. In addressing this issue, a novel compensation model architecture based on autoencoder bidirectional long short-term memory is proposed to adaptively extract features and reduce the parameter dimensionality, and thus enhance self-adaptability. In addition, the temporal dependencies among historical magnetic data along positive and negative directions are learned to effectively reduce the prediction error. Furthermore, two loss functions are defined to mitigate the degradation of prediction performance due to the dataset shift between calibration and measurement flights and to reduce the sensitivity of the interference prediction to geomagnetic noise. To verify the proposed method, we developed a dedicated compensator and built a flight test platform. The results show that the improvement ratio of magnetic interference on the verification flight for our method reaches 28.36, which is significantly higher than the ratios for the existing methods and the state-of-the-art commercial compensator, resulting in better interference mitigation. From the calibration flight to the verification flight, the improvement ratio of our method decreases by only 2%, resulting in a satisfactory generalization.

Aeromagnetic unmanned helicopters generate more complex maneuvers during survey flights compared to the conventional manned and unmanned aerial vehicles, thus causing a stronger multicollinearity in the coefficient estimation for the magnetic interference compensation. This inevitably degrades both the precision and stability of aeromagnetic compensation. Because the existing aeromagnetic compensation methods can only degrade a single adverse effect, they are unsuitable for special unmanned helicopter-based aeromagnetic platforms. To address this issue, a novel method of balancing the compensation precision and stability is proposed in this paper. A regularization term incorporating a dynamic weight matrix is first introduced to constrain the feasible solution space of interference compensation coefficients, thus improving the compensation precision. For ensuring the stability of aeromagnetic compensation, a parameter optimization method for the regularization term based on the cross validation-gray wolf optimizer is also proposed to adaptively determine the optimal parameters. A dedicated unmanned helicopter-based aeromagnetic platform and a compensator were built to validate the above method. The results from two flight experiments demonstrate improvement ratios of 14.3 and 9.3 for the proposed method, corresponding to 2.0 and 3.0 times higher than those of the state-of-the-art commercial compensator, and 6.2 and 2.0 times higher than those of the Principal Component Analysis(PCA), enabling superior compensation precision. Additionally, the fluctuation in standard deviations is only 16.7% of that of the commercial compensator, and only 9.1% of that of the PCA confirming enhanced compensation stability.

The fluxgate sensor is the most widely used sensor in vector magnetic measurement. However, during long-term continuous observation, the fluxgate sensor will produce large measurement errors due to changes in ambient temperature. This paper proposes a temperature calibration method for the fluxgate sensor based on polynomial fitting to address the temperature error of fluxgate sensors. First, the effect of temperature on the performance indicators of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature calibration model of the fluxgate sensor was constructed. Compared with other temperature calibration methods, the result shows that the proposed temperature calibration method is relatively simple and can better achieve real-time calibration for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were implemented. The temperature drift was reduced from about 700 nT before calibration to about 1 nT, root mean square error RMSE = 11.7189, indicating that the proposed method has a good temperature calibration effect on the data measured by the fluxgate sensor within the variable temperature background.

Geomagnetic vector measurement can obtain more geomagnetic information, which is one of the main development directions of geomagnetic measurement. To date, the magnetic flux gate magnetometer is the main tool for geomagnetic vector measurement; however, its drift issue cannot be ignored. To address this issue, we propose a geomagnetic vector measurement method based on bias and compensation fields and then develop a high-stability FHD (total-field F, horizontal component H, and declination D) vector magnetometer based on a proton sensor and a magnetic field generator. In addition, a dedicated experimental platform is constructed to verify the performance of the developed magnetometer in a geomagnetic station. Compared to the flux gate magnetometer, the experimental results indicate that the fourth-order differential noise of the declination D, horizontal component H, and vertical component Z are 1″, 0.27, and 0.15 nT, respectively, which are all lower than those of the flux gate magnetometer. In addition, the horizontal component H and vertical component Z of the flux gate magnetometer indicate more than 30 and 60 nT startup drift, respectively; in contrast, the proposed FHD magnetometer shows an excellent stability during a 16 h observation time.

The three-axis fluxgate sensor is the most widely used sensor in vector magnetic measurement. This paper proposes a calibration method for the cumulative error of the three-axis fluxgate sensor considering the influence of temperature. Firstly, various errors of the three-axis fluxgate sensor were analyzed and the cumulative error model was established. The parameters of the sensor cumulative error model were calculated based on the Particle Swarm Optimization-Genetic Algorithm (PSO-GA) optimization algorithm. Then, the water bath heating method was used to implement scalar comparison experiments between the three-axis fluxgate sensor and a reference Overhauser magnetometer at different temperatures. Experimental results show that temperature affects the scale factor coefficients in the error calibration parameters of the three-axis fluxgate sensor, and the scale factor coefficients q_x , q_y , q_z of the three axes have similar trends with temperature. Finally, we designed validation experiments for the proposed calibration method. During long-term magnetic field measurement, using the fixed scale factor coefficient matrix Q for calibration still has a large deviation from the trend of the reference field, with the maximum deviation being about 150 nT. However, using the scale factor coefficient matrix Q( T ) that changes with temperature for calibration, the trend of the calibrated magnetic field is basically consistent with the reference field, and the maximum deviation is less than 20 nT. This means that the proposed method has a better calibration effect.

An unmanned helicopter is one of the main platforms for conducting unmanned aerial vehicle aeromagnetic measurements and combines the advantages of rotary-wing and fixed-wing unmanned aerial vehicles. However, unmanned helicopter-based aeromagnetic measurement systems face complex static magnetic noise and maneuvering magnetic interference, which limit their practical performance. To address this issue, an improved multi-channel frequency measurement algorithm for the optically pumped magnetic sensor is proposed to suppress the static magnetic noise proportional to the frequency noise generated by the random quantization error and the airborne electromagnetic interference. A novel aeromagnetic compensation method for the maneuvering magnetic interference is then proposed to weaken the negative effects of the strong multicollinearity of the attitude parameters of the unmanned helicopter on the compensation accuracy and stability by introducing a regularization term and weight matrix. In addition, dedicated software is developed for the real-time calculation and compensation of magnetic interference fields. A dedicated unmanned-helicopter-based aeromagnetic measurement system is developed, and ground and flight experiments are carried out. The ground test results indicate that the static noise of the proposed system is only 0.000 82 nT. In the flight experiments, the system achieves an improvement ratio of 8.33, which is higher than the improvement ratio of 4.37 for a state-of-the-art commercial compensator. Furthermore, the dynamic noise after compensation decreases by 37.6% from 0.0157 to 0.0098 nT.

Multifunctional magnetometers enable high-precision observations of the geomagnetic direction and total magnetic field, which have broad applications in geoscience research and space weather forecasting. However, inconsistencies still occur between the optical and magnetic axes of classic multifunctional magnetometers, and few studies have investigated their direct detection. To address this issue, we propose a novel method for the detection of the optical-magnetic axis inconsistency. The magnetic azimuths for the magnetic axis and the optical axis of the multifunctional magnetometer are determined through the bias field loading and fluxgate theodolite, thereby allowing direct detection of the inconsistency. The proposed method is also modeled to quantify the detection errors introduced by the total-field sensor and the observation operation. In addition, we develop a dedicated experimental platform and conduct comparative tests at a geomagnetic observatory to validate the practicality and performance of the method. The experimental results show that the proposed method can significantly reduce the deviation of the declination curve measured by the multifunctional magnetometer relative to the curve measured by the fluxgate theodolite from 3959′′ to 19′′, achieving a 99.5% reduction rate.

Accurate measurement of geomagnetic field inhomogeneity commonly necessitates the use of two or more magnetic sensors for differential measurements, and most of the geomagnetic sensors are unusable in large inhomogeneous fields. To address these issues, this paper presents a new approach for estimating the geomagnetic field’s inhomogeneities based on a single Overhauser magnetometer. Firstly, we establish the improved free induction decay (FID) signal model by integrating the phases of all protons over the entire Overhauser sensor in arbitrary inhomogeneous fields, which enables the inversion of the geomagnetic field gradient using the relaxation characteristics of the FID signal. Then, we propose a composite algorithm designed to accurately derive the relaxation parameters of the FID signal by carefully extracting and denoising its envelope, and after that calculate the gradient of the geomagnetic field by the above FID signal model. Moreover, we designed a specialized Overhauser magnetic sensor prototype for measuring geomagnetic gradients and conducted experiments on a dedicated experimental platform. The designed prototype successfully measured the magnetic gradient even under high gradients of up to 10,005 nT/m, yielding a measurement error of 15.83%, with one sensor in the experiments. Additionally, we employed this method to successfully detect unexploded ordnance (UXO) using the transverse relaxation time of the FID signal as an indicator. This application further validates the effectiveness and practicality of our proposed methodology.