In this article, we consider the problem of extracting star spots for traditional star sensors with short exposure time under high dynamic conditions (>5°/s). Under this setting, the signal-to-noise ratio (SNR) of star spots decreases visibly, resulting in less star information being presented, making most of star spots undetectable. We propose a spatial-temporal star spots extraction for this task. According to the constraints of angular distance and kinematic of stars in multi-frame images, a detection method based on spatial-temporal filtering is first proposed to detect star spots with low SNR. It establishes a star spot observation by using the correlation between its predicted image and measured image, which fuses the measurement information of the star and the motion information of the star sensor, and detects star spots based on the maximum a posteriori (MAP). Then, according to the imaging characteristics that the imaging region of a star spot can be regarded as a rectangle, an extraction method based on hypothesis-verification is developed to completely extract pixels of the star spot. It establishes the classification probability of a pixel by the multi-layer Gaussian mixture model, and extract pixels according to the maximum likelihood estimation (MLE) of the star image. Experiments demonstrate the extraction capability and accuracy of the method. In conclusion, there is a promising application value for the method to improve the attitude estimation accuracy and update rate of star sensor under high dynamic conditions.

Single-pixel imaging is a computational optical imaging technique that uses a single-pixel detector to obtain scene information and reconstruct the image. Compared with traditional imaging techniques, single-pixel imaging has the advantages of high sensitivity and a wide dynamic range, etc., which make it have broad application prospects in special frequency band imaging and scattering media imaging. This paper mainly introduces the history of development and the characteristics of the single-pixel detector, focuses on the typical applications of single-pixel imaging in coded aperture, transverse scanning, and longitudinal scanning systems, and gives an account of the application of deep learning technology in single-pixel imaging. At the end of this paper, the development of single-pixel imaging is summarized and future trends forecasted.

Spatial multispectral imaging technology can selectively image in specific spectral bands, and the filter wheel is a core component for multispectral selection. At present, there are relatively few types of spectral bands for the filter wheel under limited space/weight constraints. Addressing the challenges presented by this issue, this paper introduces an innovative design approach for the development of a double-layer or even multi-layer filter wheel that is operated by a solitary motor in conjunction with a differential gear mechanism, enabling a vast array of spectral segment combinations within a highly compact layout. A detailed design is implemented for the double-layer filter wheel, including comprehensive modal and dynamic analyses. The results of the modal analysis attested to the structural stability of the component, and the outcomes of the dynamic analysis validated the component’s timely and reliable switching capabilities. A prototype was meticulously crafted and subjected to rigorous testing. The switching functionality was validated during these tests, concurrently affirming the accuracy of the finite element analysis results. Additionally, spectral and application testing confirmed the number of spectral segments and the practical utility of the components. The research presented in this article introduces an innovative design concept for multispectral imaging filter-wheel mechanisms, providing a valuable reference and profound insights for the design and arrangement of a double-layer or even multi-layer filter wheel.