<div style="text-align:justify;"> Due to the influence of processing technology and environmental factors, there are errors in attitude measurement with the three-axis magnetometer, and the change of p...<div style="text-align:justify;"> Due to the influence of processing technology and environmental factors, there are errors in attitude measurement with the three-axis magnetometer, and the change of parameters during the operation of the magnetometer in orbit will have a great impact on the measurement accuracy. This paper studies the calibration method of magnetometer based on BP neural network, which reduces the influence of model error on calibration accuracy. Firstly, the error model of the magnetometer and the structural characteristics of the BP neural network are analyzed. Secondly, the number of hidden layers and hidden nodes is optimized. To avoid the problem of slow convergence and low accuracy of basic BP algorithm, this paper uses the Levenberg Marquardt backpropagation training method to improve the training speed and prediction accuracy and realizes the on-orbit calibration of magnetometer through online training of the neural network. Finally, the effectiveness of the method is verified by numerical simulation. The results show that the neural network designed in this paper can effectively reduce the measurement error of magnetometer, while the online training can effectively reduce the error caused by the change of magnetometer parameters, and reduce the measurement error of magnetometer to less than 10 nT. </div>展开更多
The China Seismo-Electromagnetic Satellite(CSES) is the first platform of China's earthquake observation system in space and the first satellite of China's geophysical field detection missions. The high precis...The China Seismo-Electromagnetic Satellite(CSES) is the first platform of China's earthquake observation system in space and the first satellite of China's geophysical field detection missions. The high precision magnetometer(HPM), which contains two fluxgate sensors and a coupled dark state magnetometer(CDSM), measures the vector of the Earth's magnetic field with a bandwidth from DC to 15 Hz. The two fluxgate sensors are in a gradiometer configuration in order to reduce satellite interferences. Additionally, the CDSM sensor measures the scalar value of the magnetic field with higher accuracy and stability.Several data processing and calibration methods have been prepared to get accurate vector magnetic field data. This includes the calibration of each of the three sensors, the absolute vector correction algorithm, the spacecraft magnetic interference elimination and the coordinate transformation method. Also the instrument performances based on ground calibration activities are shown in this article.展开更多
文摘<div style="text-align:justify;"> Due to the influence of processing technology and environmental factors, there are errors in attitude measurement with the three-axis magnetometer, and the change of parameters during the operation of the magnetometer in orbit will have a great impact on the measurement accuracy. This paper studies the calibration method of magnetometer based on BP neural network, which reduces the influence of model error on calibration accuracy. Firstly, the error model of the magnetometer and the structural characteristics of the BP neural network are analyzed. Secondly, the number of hidden layers and hidden nodes is optimized. To avoid the problem of slow convergence and low accuracy of basic BP algorithm, this paper uses the Levenberg Marquardt backpropagation training method to improve the training speed and prediction accuracy and realizes the on-orbit calibration of magnetometer through online training of the neural network. Finally, the effectiveness of the method is verified by numerical simulation. The results show that the neural network designed in this paper can effectively reduce the measurement error of magnetometer, while the online training can effectively reduce the error caused by the change of magnetometer parameters, and reduce the measurement error of magnetometer to less than 10 nT. </div>
文摘The China Seismo-Electromagnetic Satellite(CSES) is the first platform of China's earthquake observation system in space and the first satellite of China's geophysical field detection missions. The high precision magnetometer(HPM), which contains two fluxgate sensors and a coupled dark state magnetometer(CDSM), measures the vector of the Earth's magnetic field with a bandwidth from DC to 15 Hz. The two fluxgate sensors are in a gradiometer configuration in order to reduce satellite interferences. Additionally, the CDSM sensor measures the scalar value of the magnetic field with higher accuracy and stability.Several data processing and calibration methods have been prepared to get accurate vector magnetic field data. This includes the calibration of each of the three sensors, the absolute vector correction algorithm, the spacecraft magnetic interference elimination and the coordinate transformation method. Also the instrument performances based on ground calibration activities are shown in this article.
