In the previous study, there were a few direct star identification (star-ID) algorithms for smearing star image. An end-to-end star-ID algorithm is proposed in this article, to directly identify the smearing image from star sensors with fast attitude maneuvering. Combined with convolutional neural networks and the self-attention mechanism of transformer encoder, the algorithm can effectively classify the smearing image and identify the star. Through feature extraction and position encoding, neural networks learn the position of stars to generate semantic information and realize the end-to-end identification for the smearing star image. The algorithm can also solve the problem of low identification rate due to smearing of long exposure time for images. A dataset of dynamic stars is analyzed and constructed based on multiple angular velocities. Experiment results show that, compared with representative algorithms, the identification rate of the proposed algorithm is improved at high angular velocities. When the three-axis angular velocity is 10°/s, the rate is still 60.4%. At the same time, the proposed algorithm has good robustness to position noise and magnitude noise.

To improve the centroid extraction accuracy and efficiency of high-dynamic star sensors, this paper proposes a multi-centroid localization method based on the prior distribution of star trail projections. First, the mapping relationship between attitude information and star trails is constructed based on a geometric imaging model, and an endpoint centroid group extraction strategy is designed from the perspectives of time synchronization and computational complexity. Then, the endpoint position parameters are determined by fitting the star trail grayscale projection using a line spread function, and accurate centroid localization is achieved through principal axis analysis and inter-frame correlation. Finally, the effectiveness of the proposed method under different dynamic scenarios was tested using numerical simulations and semi-physical experiments. The experimental results show that when the three-axis angular velocity reaches 8°/s, the centroid extraction accuracy of the proposed method remains superior to 0.1 pixels, achieving an improvement of over 30% compared to existing methods and simultaneously doubling the attitude measurement frequency. This demonstrates the superiority of this method in high-dynamic attitude measurement tasks.

Measuring resident space objects (RSOs) by star trackers has become a research hotspot in space situational awareness. However, the arc length measured by star trackers is too short to complete the high-precision orbit determination of the RSO. In this paper, a staring–tracking measurement (STM) method is proposed to obtain a sufficiently long arc. Firstly, the three-axis attitude calculation model of the satellite is established for tracking RSOs during the staring process. Secondly, an observation method of the RSO location is proposed based on the principle of the angular distance invariance and the imaging mapping function of the star tracker. Finally, the numerical simulations and the ground experiment were conducted to verify the proposed algorithm. Simulation results show that the acquisition time of the measurable arcs is longer than 6 min, and the accuracy of the declination and the ascension can meet the standard for RSO orbit determination. The physical experiment shows that the acquisition time is consistent with that in the simulation. Our work provides a new idea for the realization of high-precision space-based optical measurement.

Reference-based super-resolution (RefSR) has achieved remarkable progress and shows promising potential applications in the field of remote sensing. However, previous studies heavily rely on existing and high-resolution reference image (Ref), which is hard to obtain in remote sensing practice. To address this issue, a novel structure based on a zoom camera structure (ZCS) together with a novel RefSR network, namely AEFormer, is proposed. The proposed ZCS provides a more accessible way to obtain valid Ref than traditional fixed-length camera imaging or external datasets. The physics-enabled network, AEFormer, is proposed to super-resolve low-resolution images (LR). With reasonably aligned and enhanced attention, AEFormer alleviates the misalignment problem, which is challenging yet common in RefSR tasks. Herein, it contributes to maximizing the utilization of spatial information across the whole image and better fusion between Ref and LR. Extensive experimental results on benchmark dataset RRSSRD and real-world prototype data both verify the effectiveness of the proposed method. Hopefully, ZCS and AEFormer can enlighten a new model for future remote sensing imagery super-resolution.

The non-linearly curved coastal zone is very long and wide. The traditional satellite can’t cover the whole coastal zone in a single scan. So, the method of matching imaging combining the adjustment of satellite attitude and the scanning of the swing mirror are proposed. Firstly, based on the position of feature points of the coastal zone, the attitude when the optical axis of the satellite always points to the feature points is calculated. According to the width of the sea and land on both sides of the coastline, the parameters of the swing mirror of wide-swath whiskbroom payloads are analyzed. Secondly, the velocity vector model considering time-varying satellite attitude and the dynamic scanning of the swing mirror is constructed. The schemes of matching imaging such as adjustments of yaw angle and detector are developed. Finally, the precise experiment is designed to verify the correctness of the matching imaging. The experimental results show that the resolution of the matching imaging is less than 1 pixel and its modulation transfer function (MTF) is greater than the human eye’s minimum MTF of 0.026. The method of matching imaging using the adjustment of satellite attitude and the scanning of the swing mirror can realize wide imaging along the coastline and improve the temporal resolution at the same time.

The rolling shutter effect decreases the accuracy of the attitude measurement of star trackers when they work in rolling shutter exposure mode, especially under dynamic conditions. To solve this problem, a rolling shutter effect correction method based on particle swarm optimization is proposed. Firstly, a collinear reverse installation method between the star tracker and the satellite is proposed, which simplifies the relationship between the velocity of the star centroid and the star tracker angular velocity. Next, the centroid error model is obtained by the star centroid velocity. Based on the centroid error model and angular distance invariance, the loss function of the centroid error is proposed. Then, the particle swarm optimization algorithm is used to determine the star tracker angular velocity by minimizing the loss function. Finally, the simulation and experiments are carried out to verify the proposed method. The experimental results show that the convergence times of the algorithm are less than 50 and the root mean square error (RMSE) of the angular velocity is better than 0.02°/s when the angular velocity of the star tracker is no more than 5°/s.