A Complement Method for Magnetic Data Based on TCN-SE Model

Abstract

The magnetometer is a vital measurement component for attitude measurement of near-Earth satellites and autonomous magnetic navigation, and monitoring health is significant. However, due to the compact structure of the microsatellites, the stray magnetic changes caused by the complex working conditions of each system will inevitably interfere with the magnetometer measurement. In addition, due to the limited capacity of the satellite–ground measurement channels and the telemetry errors caused by the harsh space environment, the magnetic data collected by the ground station are partially missing. Therefore, reconstructing the telemetry data on the ground has become one of the key technologies for establishing a high-precision magnetometer twin model. In this paper, firstly, the stray magnetic interference is eliminated by correcting the installation matrix for different working conditions. Then, the autocorrelation characteristics of the residuals are analyzed, and the TCN-SE (temporal convolutional network-squeeze and excitation) network with long-term memory is designed to model and extrapolate the historical residual data. In addition, MAE (mean absolute error) is used to analyze the data without missing at the corresponding time in the forecast period and decreases to 74.63 nT. The above steps realize the accurate mapping from the simulation values to the actual values, thereby achieving the reconstruction of missing data and establishing a solid foundation for the judgment of the health state of the magnetometer.

Application of bioinspired geomagnetic sensor measurements and geomagnetic map modeling based on neural networks in simulated navigation

“…In recent years, combined research in the fields of geomagnetic navigation and neural networks has become increasingly common. ANNs can establish highly accurate geomagnetic databases by predicting and supplementing the existing geomagnetic information [22][23][24]. In addition, they have been proven to improve the positioning accuracy of geomagnetic matching navigation [25][26][27].…”