To build a long-wave infrared catadioptric optical system for deep space low-temperature target detection with a lightweight and wide field of view, this work conducted a study that encompasses a local cooling optical system, topology optimization-based metal mirror design, and additive manufacturing. First, a compact catadioptric optical system with local cooling was designed. This system features a 55 mm aperture, a 110 mm focal length, and a 4-degree by 4-degree field of view. Secondly, we applied the principles of topology optimization to design the primary mirror assembly, the secondary mirror assembly, and the connecting baffle. The third and fourth modes achieved a resonance frequency of 1213.7 Hz. Then, we manufactured the mirror assemblies using additive manufacturing and single-point diamond turning, followed by the centering assembly method to complete the optical assembly. Lastly, we conducted performance testing on the system, with the test results revealing that the modulation transfer function (MTF) curves of the optical system reached the diffraction limit across the entire field of view. Remarkably, the system’s weight was reduced to a mere 96.04 g. The use of additive manufacturing proves to be an effective means of enhancing optical system performance.

Since the atmosphere has a strong scattering effect on ultraviolet light, the transmission of non-line-of-sight (NLOS) signals can be realized in the atmosphere. In previous articles, ultraviolet (UV) light atmospheric scattering has been characterized by many scattering models based on spot light sources with uniformly distributed light intensity. In order to explore the role of light sources in atmospheric transmission, this work proposed a UV light atmospheric transport model under different types of light source, including light-emitting diode (LED), laser, and ordinary light sources, based on the Monte Carlo point probability method. The simulation of the light source in the proposed model is a departure from the use of a light source with uniform intensity distribution in previous articles. The atmospheric transmission efficiency of different light sources was calculated and compared with the data of existing models. The simulation results showed that the type of light source can significantly change the shape of the received signal and the received energy density. The Monte Carlo (MC) point probability method dramatically reduced the calculation time and the number of photons. The transmission characteristics of different ultraviolet light sources in the atmosphere provide a theoretical foundation for the design of ultraviolet detection and near-ultraviolet signal communication in the future.

Image detection technology is of paramount importance across various fields. This significance is not only seen in general images with everyday scenes but also holds substantial research value in the field of remote sensing. Remote sensing images involve capturing images from aircraft or satellites. These images typically feature diverse scenes, large image formats, and varying imaging heights, thus leading to numerous small-sized targets in the captured images. Accurately identifying these small targets, which may occupy only a few pixels, is a challenging and active research area. Current methods mainly fall into two categories: enhancing small target features by improving resolution and increasing the number of small targets to bolster training datasets. However, these approaches often fail to address the core distinguishing features of small targets in the original images, thus resulting in suboptimal performance in fine-grained classification tasks. To address this situation, we propose a new network structure DDU (Downsample Difference Upsample), which is based on differential and resolution changing methods in the Neck layer of deep learning networks to enhance the recognition features of small targets, thus further improving the feature richness of recognition and effectively solving the problem of low accuracy in small target object recognition. At the same time, in order to take into account the recognition effect of targets of other sizes in the image, a new attention mechanism called PNOC (protecting the number of channels) is proposed, which integrates small target features and universal object features without losing the number of channels, thereby increasing the accuracy of recognition. And experimental verification was conducted on the PASCAL-VOC dataset. At the same time, it was applied to the testing of the fine-grained MAR20 dataset and found that the performance was better than other classic algorithms. At the same time, because the proposed framework belongs to a one-stage detection method, it has good engineering applicability and scalability, and universality in scientific research applications are good. Through comparative experiments, it was found that our algorithm improved the performance of the mAP by 0.7% compared to the original YOLOv8 algorithm.

In order to realize the compact layout of aerospace payloads, the design and manufacture of high-steepness lightweight aluminum alloy mirrors is a key technology to be explored. For high-steepness mirrors, the traditional method is to establish the initial thickness that satisfies the bending stiffness through finite element optimization iteration, which cannot achieve fast modeling and performance estimation. In this paper, firstly, the equivalent modeling method of the mirror with high steepness is proposed to achieve the equivalent of the elliptic mirror with a diameter of 410 × 310 mm and F# less than 0.7. Based on the mathematical model, topology shape optimization was used to build a highly lightweight mirror structure that could be quickly assembled, and the equivalent area–mass density of the mirror is less than 34 kg/mm2. Next, the rationality of design feasibility was verified by simulation analysis. Finally, by using single point diamond turning combined with post polishing process, the high-precision manufacturing of conventional aluminum alloy mirror was realized. The results show that the mirror shape accuracy is 1/10 λ (λ = 632.8 nm), and the surface roughness Ra is 3.342 nm. This research provides strong theoretical support and application prospects for the low-cost and rapid manufacturing of high-steepness lightweight aluminum alloy mirrors.

Step/stare imaging with focal plane arrays (FPAs) has become the main approach to achieve wide area coverage and high resolution imaging for long range targets. A fast steering mirror (FSM) is usually utilized to provide back-scanned motion to compensate for the image motion. However, the traditional optical design can just hold one field point relatively stable, typically the central or on-axis field point, on the FPA during back-scanning; all other field points may wander during the exposure due to imaging distortion characteristics of the optical system, which reduces the signal to noise ratio (SNR) of the target. Aiming toward this problem, this paper proposes a non-rotationally symmetric field mapping method for the back-scanned step/stare imaging system, which can make all field points stable on the FPA during back-scanning. First of all, the mathematical model of non-rotationally symmetric field mapping between object space and image space is established. Then, a back-scanned step/stare imaging system based on the model is designed, in which this non-rotationally symmetric mapping can be implemented with an afocal telescope including freeform lenses. Freeform lenses can produce an anamorphic aberration to adjust distortion characteristics of the optical system to control image wander on an FPA. Furthermore, the simulations verify the effectiveness of the method.