In this paper, a metal-dielectric-metal structure based on a Fabry–Perot cavity was proposed, which can provide near 100% perfect narrow-band absorption. The lossy ultrathin silver film was used as the top layer spaced by a lossless silicon oxide layer from the bottom silver mirror. We demonstrated a narrow bandwidth of 20 nm with 99.37% maximum absorption and the absorption peaks can be tuned by altering the thickness of the middle SiO₂ layer. In addition, we established a deep understanding of the physics mechanism, which provides a new perspective in designing such a narrow-band perfect absorber. The proposed absorber can be easily fabricated by the mature thin film technology independent of any nano structure, which make it an appropriate candidate for photodetectors, sensing, and spectroscopy.

Under space-based observation conditions, targets are subject to a large number of stars, clutter, false alarms, and other interferences, which can significantly impact the traditional Gaussian mixture probability hypothesis density (GM-PHD) filtering method, leading to tracking biases. To enhance the capability of the traditional GM-PHD method for multi-target tracking in space-based platform observation scenarios, in this article, we propose a GM-PHD algorithm based on spatio-temporal pipeline filtering and enhance the conventional spatio-temporal pipeline filtering method. The proposed algorithm incorporates two key enhancements: firstly, by adaptively adjusting the pipeline’s central position through target state prediction, it ensures continuous target tracking while eliminating noise; secondly, by computing trajectory similarity to distinguish stars from targets, it effectively mitigates stellar interference in target tracking. The proposed algorithm realizes a more accurate estimation of the target by constructing a target state pipeline using the time series and correlating multiple frames of data to achieve a smaller optimal sub-pattern assignment (OSPA) distance and a higher tracking accuracy compared with the traditional algorithm. Through simulations and real-world data validation, the algorithm showcased its capability for multi-target tracking in a space-based context, outperforming traditional methods and effectively addressing the challenge of stellar interference in space-based multi-target tracking.

In this study, we propose and fabricate a perfect absorber on a planar substrate using alternate silicon dioxide and ultrathin metallic lossy chromium (Cr) films. Furthermore, we transfer the absorber to a curved substrate via an optimization design of symmetric structures. The absorber exhibits a highly efficient absorption and large incident-angular tolerance characteristics in the whole visible region. We investigate each layer contribution to the absorption theoretically, and find that ultrathin (~5 nm) lossy Cr films play a dominant absorptive role. Using the effective interface method, we calculate the phase difference on the lossy Cr front surface. It is close to the destructive interference condition, from which we clarify why the proposed structures can produce a highly efficient absorption.

To address the limitations of traditional methods in feature extraction and multi-modal information fusion, this paper proposes an infrared–visible image object detection architecture that integrates Convolutional Neural Networks (CNNs) and Deformable Transformers. This method leverages the advantages of CNN in local feature modeling and the capabilities of Transformer in capturing global contextual information, facilitating the fusion of semantic consistency and structural details across modalities. By introducing a detection-aware multi-task optimization mechanism, the model improves object detection in challenging scenarios such as low-light conditions, occlusion, and complex backgrounds. Experiments on multiple standard datasets, including M3FD and LLVIP, indicate that the proposed method achieves competitive or better performance than the compared methods in key metrics such as mAP. Specifically, our method obtains the best mAP50 among the evaluated methods with an mAP50 of 74.2% on the M3FD dataset and 98.6% on the LLVIP dataset, surpassing the second-best PIAFusion by 4.3% and 2.5% respectively. These quantitative results support the practicality and effectiveness of our approach in the evaluated complex environments.