Measuring the pose of non-cooperative targets in space is a critical supporting technology for cleaning up space debris and recovering items. However, most existing methods are simulation experiments conducted in good lighting environments and tend to show poor performance in dark lighting environments. A target pose measurement method based on binocular vision is proposed, which is suitable for dark lighting environments. First, the traditional features from accelerated segment test algorithm are improved to reduce the influence of illumination on the performance of feature point extraction under various postures. The point feature and line feature are combined to extract image features more easily in a dark lighting environment while retaining the high accuracy of the pose measurement algorithm based on point features. Second, the normalized cross-correlation coefficient matching method is combined with the epipolar constraint to narrow the search range of the matching points from the two-dimensional plane to the epipolar line, which substantially improves the matching efficiency and accuracy of the matching algorithm. Finally, post-processing through feature matching is performed to reduce the probability of mismatches. Simulation and physical experiment results show that our method can stably extract features and obtain high-precision target pose information in well-illuminated as well as dark lighting environments, making it suitable for high-precision target pose measurement under insufficient illumination.

Particle filters have been widely used in dim and small target tracking, which plays a significant role in navigation applications. However, their characteristics, such as difficulty of expressing features for dim and small targets and lack of particle diversity caused by resampling, lead to a considerable negative impact on tracking performance. In the present paper, we propose an improved resampling particle filter algorithm based on adaptive multi-feature fusion to address the drawbacks of particle filters for dim and small target tracking and improve the tracking performance. We first establish an observation model based on the adaptive fusion of the features of the weighted grayscale intensity, edge information, and wavelet transform. We then generate new particles based on residual resampling by combining the target position in the previous frame and the particles in the current frame with higher weights, with the tracking accuracy and particle diversity improving simultaneously. The experimental results demonstrate that our proposed method achieves a high tracking performance with a distance accuracy of 77.2% and a running speed of 106 fps, respectively, meaning that it will have a promising prospect in dim and small target tracking applications.

The acquisition of nighttime remote-sensing visible-light images is often accompanied by low-illumination effects and haze interference, resulting in significant image quality degradation and greatly affecting subsequent applications. Existing low-light enhancement and dehazing algorithms can handle each problem individually, but their simple cascade cannot effectively address unknown real-world degradations. Therefore, we design a joint processing framework, WFDiff, which fully exploits the advantages of Fourier–wavelet dual-domain features and innovatively integrates the inverse diffusion process through differentiable operators to construct a multi-scale degradation collaborative correction system. Specifically, in the reverse diffusion process, a dual-domain feature interaction module is designed, and the joint probability distribution of the generated image and real data is constrained through differentiable operators: on the one hand, a global frequency-domain prior is established by jointly constraining Fourier amplitude and phase, effectively maintaining the radiometric consistency of the image; on the other hand, wavelets are used to capture high-frequency details and edge structures in the spatial domain to improve the prediction process. On this basis, a cross-overlapping-block adaptive smoothing estimation algorithm is proposed, which achieves dynamic fusion of multi-scale features through a differentiable weighting strategy, effectively solving the problem of restoring images of different sizes and avoiding local inconsistencies. In view of the current lack of remote-sensing data for low-light haze scenarios, we constructed the Hazy-Dark dataset. Physical experiments and ablation experiments show that the proposed method outperforms existing single-task or simple cascade methods in terms of image fidelity, detail recovery capability, and visual naturalness, providing a new paradigm for remote-sensing image processing under coupled degradations.

In view of the intelligent requirements of spatial non-cooperative target detection and recognition tasks, this paper applies the deep learning method YOLOX_L to the task and draws on YOLOF (You Only Look One-Level Feature) and TOOD (Task-Aligned One-Stage Object Detection), which optimize and improve its detection accuracy to meet the needs of space Task Accuracy Requirements. We improve the FPN (Feature Pyramid Networks) structure and decoupled prediction network in YOLOX_L and perform a validation comparative analysis of the improved YOLOX_L on the VOC2007+2012 and spacecraft dataset. Our experiments conducted on the VOC2007+2012 benchmark show that the proposed method can help YOLOX_L achieve 88.86 mAP, which is higher than YOLOX_L, running at 50 FPS under the image size of 608 × 608. The spatial target detection method based on the improved YOLOX has a detection accuracy rate of 96.28% and a detection speed of 50 FPS on our spacecraft dataset, which prove that the method has certain practical significance and practical value.

The problem of inadequate object detection accuracy in complex remote sensing scenarios has been identified as a primary concern. Traditional YOLO-series algorithms encounter challenges such as poor robustness in small object detection and significant interference from complex backgrounds. In this paper, a multi-scale feature fusion framework based on an improved version of YOLOv8_L is proposed. The combination of a graph attention network (GAT) and Dilated Encoder network significantly improves the algorithm detection and recognition performance for space remote sensing objects. It mainly includes abandoning the original Feature Pyramid Network (FPN) structure, proposing an adaptive fusion strategy based on multi-level features of backbone network, enhancing the expression ability of multi-scale objects through upsampling and feature stacking, and reconstructing the FPN. The local features extracted by convolutional neural networks are mapped to graph-structured data, and the nodal attention mechanism of GAT is used to capture the global topological association of space objects, which makes up for the deficiency of the convolutional operation in weight allocation and realizes GAT integration. The Dilated Encoder network is introduced to cover different-scale targets by differentiating receptive fields, and the feature weight allocation is optimized by combining it with a Convolutional Block Attention Module (CBAM). According to the characteristics of space missions, an annotated dataset containing 8000 satellite and space station images is constructed, covering a variety of lighting, attitude and scale scenes, and providing benchmark support for model training and verification. Experimental results on the space object dataset reveal that the enhanced algorithm achieves a mean average precision (mAP) of 97.2%, representing a 2.1% improvement over the original YOLOv8_L. Comparative experiments with six other models demonstrate that the proposed algorithm outperforms its counterparts. Ablation studies further validate the synergistic effect between the graph attention network (GAT) and the Dilated Encoder. The results indicate that the model maintains a high detection accuracy under challenging conditions, including strong light interference, multi-scale variations, and low-light environments.

Satellite laser communication is a promising technology for the next-generation communication system. Its communication performance is subject to the APT beam-pointing accuracy. One of the most important problems is reducing the coaxiality error before the APT starts working. However, the coaxiality error is difficult to correct effectively owing to the lack of empirical guidance based on qualitative analysis. We study the inducement that will generate coaxiality errors. The mathematical model of the influence of the CCR dihedral angle error and planeness error on the spot centroid measurement are built, and an analysis is performed. The model of the beam-pointing error induced by the APT element’s assembly error is built, and the pointing error change rule is explored. Furthermore, the coaxiality performance simulation is performed in the presence of a CCR geometrical error while considering the assembly error. The results show that the coaxiality error has a nonlinear characteristic. The CCR planeness error has a greater influence on coaxiality deviation than that of dihedral angle error under certain conditions. This research is relevant to the design and test work of the APT system.