In the South Atlantic Anomaly (SAA) area, due to the influence of transient noise caused by space radiation, a star sensor can easily stay in the lost-in-space (LIS) mode for a long time. To solve this problem, this paper proposes a fast star identification (FSI) algorithm. First, a noise suppression method based on scale assessment and neighborhood comparison is developed. Next, a fast and accurate search technique of multiple main stars based on the k-vector technique is used to realize star identification. The search technique builds a self-defined attribute database of stars, and a fast search method of a repeated star identity is proposed to realize the positioning of the main star. Lastly, the final main stars are obtained through the comparison of field of view and verification of angular distance. The experimental results showed that when the star sensor works at a speed of 0.1°/s and the level of transient noise signals is lower than 900, the successful identification rate is higher than 70%. In addition, compared with the triangle algorithm, match group algorithm, and multi-pole algorithm (MPA), the proposed FSI algorithm has the advantages of a higher successful identification rate and a faster execution speed.

The interference caused by stray light leads to the invalid attitude of star sensors in orbit, thus affecting the attitude control of satellites. In order to overcome this problem, this paper proposes a fast star-detection algorithm with strong stray-light suppression ability. The first step in the proposed method is stray-light suppression. The highlighted pixels are unified and then erosion and dilation operations based on a large template are performed. Using the background image only, which is filled with stray light, the cleaner star image is obtained by subtracting the background from the unified image. The second step in the proposed method is binarization. The binary star image is obtained by using a line-segment strategy combined with a local threshold. The third step in the proposed method is star labeling. It comprises connected-domain labeling based on the preordering of pixels and the calculation of centroid coordinates of stars in each connected domain. The experimental results show that the proposed algorithm extracts the stars stably under the interference of different stray lights. The proposed method consumes less resources, and the output delay is only 18.256 us. Moreover, the successful identification rate is 98% and the attitude accuracy of the X and Y axes is better than 5″(3σ) when the star sensor works at the speed of zero.

Single-pixel noise commonly appearing in a star sensor can cause an unexpected error in centroid extraction. To overcome this problem, this paper proposes a star image denoising algorithm, named Improved Gaussian Side Window Filtering (IGSWF). Firstly, the IGSWF algorithm uses four special triangular Gaussian subtemplates for edge protection. Secondly, it exploits a reconstruction function based on the characteristic of stars and noise. The proposed IGSWF algorithm was successfully verified through simulations and evaluated in a star sensor. The experimental results indicated that the IGSWF algorithm performed better in preserving the shape of stars and eliminating the single-pixel noise and the centroid estimation error (CEE) value after using the IGSWF algorithm was eight times smaller than the original value, six times smaller than that after traditional window filtering, and three times smaller than that after the side window filtering.

Stray light, such as sunlight, moonlight, and earth-atmosphere light, can bring about light spots in backgrounds, and it affects the star detection of star sensors. To overcome this problem, this paper proposes a star detection algorithm (CMLCM) with multidirectional local contrast based on curvature. It regards the star image as a spatial surface and analyzes the difference in the curvature between the star and the background. It uses a facet model to represent the curvature and calculate the second-order derivatives in four directions. According to the characteristic of the star and the complex background, it enhances the target and suppresses the complex background by a new calculation method of a local contrast map. Finally, it divides the local contrast map into multiple 256 × 256 sub-regions for a more effective threshold segmentation. The experimental results indicated that the CMLCM algorithm could effectively detect a large number of accurate stars under stray light interference, and the detection rate was higher than other compared algorithms with a lower false alarm rate.

To address the lack of reliable test data for evaluating star sensor performance in dynamic airborne environments, this paper presents a high-fidelity star map simulation method for all-time three-Field of View (FOV) star sensors. A comprehensive simulation framework integrating stellar radiation, atmospheric transmission, and detector noise models was developed to accurately model star trailing effects under dynamic conditions. First, a stellar position calculation model incorporating atmospheric refraction correction and platform motion parameters was established through coordinate transformations between the Geocentric Celestial Reference System (GCRS) and FOV coordinate system. Next, a complete energy transfer chain was constructed by combining star catalog data, atmospheric radiative properties, and detector noise characteristics. Finally, a quantitative evaluation system was introduced, employing metrics such as signal-to-noise ratio (SNR), total grayscale value (G_total), grayscale concentration index (GCI), and dynamic star displacement (DSD). Field experiments at 2388 m altitude (100.23°E, 26.86°N) demonstrated the average relative error of all evaluation metrics below 9% for static conditions and approximately 8% for dynamic scenarios between simulated and real star maps. The method effectively reproduces stellar radiation, atmospheric noise, and dynamic degradation, providing reliable simulation conditions for airborne star sensor testing and star trailing restoration algorithm development.

Stripe-like space target detection (SSTD) plays a crucial role in advancing space situational awareness, enabling missions like satellite navigation and debris monitoring. Existing unsupervised methods often falter in low signal-to-noise ratio (SNR) conditions, while fully supervised approaches require extensive and labor-intensive pixel-level annotations. To address these limitations, this paper introduces MRSA-Net, a novel encoder-decoder network specifically designed for SSTD. MRSA-Net incorporates multi-receptive field processing and multi-level feature fusion to effectively extract features of variable and low-SNR stripe-like targets. Building upon this, we propose the Collaborative Static-Dynamic Teaching (CSDT) architecture, a semi-supervised learning architecture that reduces reliance on labeled data by leveraging both static and dynamic teacher models. The framework uses the straight-line prior of stripe-like targets to customize linearity and presents an innovative Adaptive Pseudo-Labeling (APL) strategy, dynamically selecting high-quality pseudo-labels to enhance the student model’s learning process. Extensive experiments on AstroStripeSet and other real-world datasets demonstrate that the CSDT framework achieves state-of-the-art performance in SSTD. Using just 1/16 of the labeled data, CSDT outperforms the second-best Interactive Self-Training Mean Teacher (ISMT) method by 2.64% in mean Intersection over Union (mIoU) and 4.5% in detection rate ($P_d$), while exhibiting strong generalization in unseen scenarios. This work marks the first application of semi-supervised learning techniques to SSTD, offering a flexible and scalable solution for challenging space imaging tasks.