A SLIC-DBSCAN Based Algorithm for Extracting Effective Sky Region from a Single Star Image

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

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.

On-Orbit Geometric Distortion Correction on Star Images through 2D Legendre Neural Network

The star tracker is a prerequisite device to realize high-precision attitude determination for a spacecraft. However, due to the errors in optical lens machining, optical path assembly, and temperature alternation, optical instruments suffer from some amount of optical geometric distortion, resulting in declining star tracker accuracy. The on-orbit distortion correction of star images is indispensable for precise performance. In this paper, a novel single-layer 2D Legendre neural network (2DLNN) to automatically correct the geometric distortion of the star tracker is proposed. An offline training method grounded on batch star images and an online training algorithm based on sequential star images are designed, respectively. The 2DLNN realizes the ground-based and on-orbit online correction of optical geometric distortion for the star tracker. The 2DLNN features self-learning, lifelong learning, and good adaptability. The single-layer neural network is simple, quick convergence, which is suitable for on-orbit implementation. The simulations demonstrate that the average distortion error can be reduced to less than 0.04 px after ground-based training. In the earth-orientation mode of the LEO satellite, the on-orbit sequential training algorithm can converge in 2500 star images under 1 frame/s. The proposed 2DLNN can achieve high-precision correction at the sub-pixel level, effectively improving the star tracker’s attitude determination accuracy.