Space non-cooperative target recognition is crucial for on-orbit servicing. Multi-satellite cooperation has great potential for broadening the observation scope and enhancing identification efficiency. However, there is currently a lack of research on recognition methods tailored for multi-satellite cooperative observation. In this paper, we propose a novel space non-cooperative target recognition method to identify satellites and debris in images from multi-satellite observations. Firstly, we design an image-stitching algorithm to generate space-wide-area images. Secondly, we propose a two-stage multi-target detection model, a lighter CNN model with distance merge threshold (LCNN-DMT). Specifically, in the first stage, we propose a novel foreground extraction model based on a minimum bounding rectangle with the threshold for distance merging (MBRT-D) to address redundant detection box extraction for satellite components. Then, in the second stage, we propose an improved SqueezeNet model by introducing separable convolution and attention mechanisms for target classification. Moreover, due to the absence of a public multi-target detection dataset containing satellites and debris, we construct two space datasets by introducing a randomized data augmentation strategy. Further experiments demonstrate that our method can achieve high-precision image stitching and superior recognition performance. Our LCNN-DMT model outperforms mainstream algorithms in target localization accuracy with only 0.928 M parameters and 0.464 GFLOPs, making it ideal for on-orbit deployment.

Multimodal fusion detection has proven to be a practical approach for improving small target perception in remote sensing. Existing studies primarily focus on the fusion of multimodal spatial-domain features, while paying insufficient attention to cross-domain differential features. Furthermore, exploration of lightweight fusion mechanisms tailored for deployment on edge devices such as UAVs remains relatively limited.To address these issues, we propose an extremely lightweight cross-modal and cross-domain differential feature fusion network (C²DFF-Net). The network includes three innovative plug-and-play modules. Specifically, we propose a cross-modal differential feature interaction (CDFIM) module to facilitate inter-modal information interaction by enhancing differential features between visible and infrared modalities. To improve the adaptive fusion of multimodal complementary features, we propose a cross-domain gated self-attention (CGSA) module. The global context information of the image is explored from a frequency-domain perspective, and a polarized self-attention mechanism is introduced to establish long-range dependencies on spatial-frequency domain differential features while filtering redundant information. Additionally, we design an adaptive light-aware mask (ALM) module to enable the network to learn effective multimodal complementary features without bias, improving its robustness in complex lighting environments such as exposure and glare. Comprehensive experiments demonstrate that C²DFF-Net not only achieves state-of-the-art (SOTA) performance but also maintains a lightweight design, achieving 85.7% mAP50 with 6.58M parameters and 14.6 GFLOPs on the DroneVehicle dataset. Furthermore, it shows a favorable accuracy-computation tradeoff compared to SOTA models on the VEDAI and FLIR datasets. Finally, we conduct real-world deployment experiments, and C²DFF-Net achieves satisfactory results under various lighting conditions, clearly demonstrating its practical applicability. Code will be available at https://github.com/FPGAzzy/C2DFF-Net.

During the in-orbit operation of spacecraft, permanent magnet synchronous motors are commonly used as power sources in the drive mechanisms of solar panel arrays and the high-precision servo control systems based on satellites. Apart from the performance of the motors themselves and the software control algorithms, the accuracy of the entire control system is also influenced by angle sensors used to detect the rotor position of the motors. As a high-precision angular measuring instrument, the inductosyn possesses excellent environmental adaptability and long service life. Effectively utilizing the inductosyn can greatly enhance the performance of servo control systems. To address the complexity of the decoding process for dual-channel round inductosyn-to-digital converters, this paper proposes a design of the decoding circuit for dual-channel round inductosyn based on the parallel-synchronization decoding method of two AD2S1210 Resolver-to-Digital Converter (RDC) decoding chips. The decoding circuit amplifies the excitation signal outputted by the AD2S1210 for driving the round inductosyn, and processes the sine and cosine induction signals outputted by the round inductosyn through filtering, amplification, and other methods; by using analog circuitry, the output signals of the dual-channel round inductosyn are processed to meet the input requirements of the AD2S1210. Finally, through both the Multisim (circuit simulation software Version 14.1) simulation and physical experiments, it was verified that the decoding circuit designed in this paper could process the input/output signals of the dual-channel round inductosyn and AD2S1210, and successfully decoded the analog induction signal of the round inductosyn. This greatly simplifies the signal decoding process for the dual-channel round inductosyn.

The process of spacecraft entry, deceleration, landing, and ascent requires high specific impulse, high reliability, and high-precision thrust adjustments for the power system. The new hybrid rocket motor adopts a complex-shaped grain and high-energy propellant, offering high-energy characteristics, continuously adjustable thrust, a relatively simple oxidant delivery system, and high reliability, making it an ideal power choice for the above systems. However, due to changes in the characteristic structure of the three-dimensional complex flame surface degradation process, it is difficult to accurately predict the motor performance. In this study, changes in the flow field structure and performance parameters during the operation of the cross-shaped grain hybrid rocket motor are presented using fuel surface reconstruction technology based on a dynamic mesh. The spatial distribution of the fuel surface is analyzed, and the accuracy of the model is verified via firing tests. The results show that the deviations of combustion chamber pressure and thrust are less than 0.6% and 1.7%, respectively. After the test, the deviation between the simulated port area and the CT-scanned port area is less than 3.5%. The accuracy of this model is verified in terms of the above two aspects, establishing a solid foundation for predicting the performance of future hybrid rocket motors with more complex-shaped grains.