In order to meet the needs of scientific research in space medicine and biology, a new multifunctional automated cell sample culture device for a Chinese space station has been designed. The temperature and carbon dioxide concentration are adjustable, making it convenient for cell culture in microgravity environments of the space station. A centrifuge is used to simulate the microgravity environment, allowing for synchronous gravity and microgravity comparison during cell culture. An automated focusing visible light microscope has been designed, capable of real-time photography of cultured cells, which can receive ground commands to complete automatic focusing and image transmission. The thermal design of the cell sample culture device uses an air heating method, and the rationality of the thermal control measures has been verified through thermal simulation analysis. The designed cell sample preparation device can monitor and display the cell growth environment parameters and device performance parameters in real time on orbit. It can also control the internal temperature within the temperature range required for cell culture. Thus, it can meet the urgent needs of various cell cultures, experiments, and scientific research on a Chinese space station.

There is an increasing number of spacecrafts in orbit, and the collision impact of high-speed moving targets, such as space debris, can cause fatal damage to these spacecrafts. It has become increasingly important to rapidly and accurately locate high-speed moving targets in space. In this study, we designed a visible-light telephoto camera for observing high-speed moving targets and a laser rangefinder for measuring the precise distance of these targets, and we proposed a method of using fast steering mirror deflection to quickly direct the emitted laser towards such targets and measure the distance. Based on the principle of photographic imaging and the precise distance of targets, a collinear equation and a spatial target location model based on the internal and external orientation elements of the camera and the target distance were established, and the principle of target location and the method for calculating target point coordinates were determined. We analyzed the composition of target point location error and derived an equation for calculating such errors. Based on the actual values of various error components and the error synthesis theory, the accuracy of target location was calculated to be 26.5 m when the target distance is 30 km (the relative velocity is 8 km/s and the velocity component perpendicular to the camera’s optical axis is less than 3.75 km/s). This study provides a theoretical basis and a method for solving the practical needs of quickly locating high-speed moving targets in space and proposes specific measures to improve target location accuracy.

Reconstructing high-quality image at low measurement rates remains a primary objective for single-pixel imaging (SPI). Deep learning-based compression reconstruction algorithm can improve the quality and reconstruction speed of the reconstructed image. Generative adversarial network (GAN) and diffusion model (DM), which have excellent performance in the field of image generation have achieved excellent research results in the field of single-pixel imaging reconstruction. GAN-based models often suffer from the mode collapse problem, and the diffusion model relies on multi-step iteration to obtain a fine and smooth forward diffusion process, but its lengthy sampling process limits the reconstruction efficiency. Therefore, we propose a single-pixel imaging reconstruction algorithm EDGN that combines the advantages of the two generation models. The generative adversarial network uses the measurement and noise to generate the initial image, the denoising diffusion probability model is used to optimize the image refinement, and the high-quality reconstructed image is obtained by joint training. Besides, an adaptive regularization coefficient limited range adjustment method (ARCLA) is designed to achieve fast convergence of coefficients. Simulations and experiments confirm that our proposed enhanced dual generative network method can significantly improve the quality of image reconstruction at low measurement rates.

The precise locations of unknown non-cooperative targets are a long-standing technical problem that needs to be solved urgently in disaster relief and emergency rescue. An imaging model of photography to a non-cooperative target was established based on the binocular vision forward intersection. The collinear equation representing the spatial position relationship between the target and its two images was obtained through coordinate system transformation, and the system of equations to calculate the geographic coordinates of the target was derived, which realized the geo-location of the unknown non-cooperative target with no control points and no source. The composition and source of the error of this target location method were analyzed, and the equation to calculate the total error of the target location was obtained according to the error synthesis theory. The accuracy of the target location was predicted. When the elevation difference between the camera and the target is 3 km, the location accuracy is 15.5 m. The same ground target was imaged by a certain type of aerial camera at different locations 3097 m above ground, and a target location verification experiment was completed. The longitude and latitude of the target obtained were compared with the true geographic longitude and latitude, and the location error of the verification experiment was calculated to be 16.3 m. The research work of this paper provides a theoretical basis and methods for the precise locations of unknown non-cooperative targets and proposes specific measures to improve the accuracy of target location.