LED solar simulators currently face limitations in their spectral simulation capabilities, especially in terms of accurately incorporating AM0G and AM1.5G solar spectra. To this end, this study introduced a framework for an LED solar spectrum simulation algorithm that considers both AM0G and AM1.5G. This study examined the principle of solar spectrum discretization and reconstruction, established a foundation for analyzing the quality of solar spectrum reconstruction, and developed a non-dominated sorting genetic algorithm II (NSGA-II)-assisted long short-term memory (LSTM)-based solar spectrum simulation strategy. This strategy integrates a multi-objective genetic algorithm to generate training datasets and a neural network for solar spectrum simulation. A dataset generation method using the NSGA-II algorithm was implemented, which leveraged the 6500 K standard blackbody spectral curve, the spectral curve offset coefficients, and the spectral distributions of various narrowband LEDs. An LSTM-based neural network for solar spectrum simulation was developed, with the RMSE serving as the evaluation function. The analysis and selection of 29 narrowband LEDs produced 5000 solar spectrum simulation training datasets. The trained LSTM model achieved spectral matching accuracies within ±10.5% and ±9.3% for AM0G and AM1.5G, respectively, meeting the A+ level simulation standard for solar spectrum reconstruction considering AM0G and AM1.5G. These findings provide a theoretical foundation and technical advancements for high-precision solar spectrum reconstruction, which has practical implications for improving the efficiency and accuracy of solar energy systems, as well as supporting further research on solar spectrum utilization, and is expected to influence the development of more efficient solar simulators.

Dark-and-weak-target simulators are used as ground-based calibration devices to test and calibrate the performance metrics of star sensors. However, these simulators are affected by full-field-of-view energy nonuniformity. This problem impacts the quality of output images and the calibration accuracy of sensors and inhibits further improvements in navigational accuracy. In the study reported in this paper, we sought to analyze the factors which affect full-field-of-view energy uniformity in dark-and-weak-target simulators. These include uneven irradiation in backlight sources, the leakage of light from LCD display panels, and the vignetting of collimating optical systems. We then established an energy transfer model of a dark-and-weak-target simulator based on the propagation of a point light source and proposed a self-adaptive compensation algorithm based on pixel-by-pixel fitting. This algorithm used a sensor to capture the output image of a dark-and-weak-target simulator and iteratively calculated the response error matrix of the simulator. Finally, we validated the feasibility and effectiveness of the compensation algorithm by acquiring images using a self-built test system. The results showed that, after compensating an output image of the dark-and-weak-target simulator, the grayscale standard display function (SDF) of the acquired sensor image was reduced by about 50% overall, so the acquisition image was more accurately compensated, and the desired level of grayscale distribution was obtained. This study provides a reference for improving the quality of output images from dark-and-weak-target simulators, so that the working environments of star sensors may be more realistically simulated, and their detection performance improved.

To meet the demand for high-precision target classification in complex scenes, a hyperspectral–polarimetric–LiDAR multimodal image fusion method tailored for few-shot scenarios is proposed. Feature-mapping functions for polarimetric and LiDAR images are constructed, and a multi-scale hierarchical optimization strategy is employed to jointly enhance low- and high-frequency components across modalities. This approach effectively addresses key challenges under limited training data, such as substantial cross-modal dimensional disparities and the difficulty of robust feature extraction and fusion. The proposed algorithm conducts bimodal image fusion on the NWPUSP spectral-polarization dataset and KAIST spectral-depth dataset. Compared with other fusion methods, it achieves average increases of 7.3% and 4.87% in information entropy, 53.18% and 30.35% in standard deviation, 48% and 108.28% in average gradient, as well as 96.25% and 101.13% in spatial frequency, respectively. Moreover, relying on the self-developed integrated hyperspectral-polarization imaging system and commercial LiDAR, we synchronously and efficiently acquire multimodal images including hyperspectral, polarization and LiDAR images of complex ground object scenes. Comparative experiments are implemented against six other mainstream fusion algorithms. The objective evaluation results show that the average improvements reach 7.19% in information entropy, 46.85% in standard deviation, 76.62% in average gradient and 79.74% in spatial frequency, which notably enhances the feature retention capability of fused images. Under few-shot conditions, the target recognition classification accuracy and Kappa coefficient of the fused image are improved by 9.8% and 11.05%, respectively, compared with those of the unimodal hyperspectral image. This effectively highlights targets under shadow occlusion and compensates for LiDAR’s response deficiencies to surface textures, achieving complementary advantages of multimodal images for ground object targets in complex scenes. This research provides a new solution for future optical multimodal remote sensing and image fusion.

High-precision measurements of direct solar radiation spectra are crucial for the development of solar resources, climate change research, and agricultural applications. However, the current measurement systems all rely on a moving two-axis tracking system with a complex structure and many error transmission links. In response to the above problems, a polar-axis rotating solar direct radiation spectroscopic measurement method is proposed, and an overall architecture consisting of a rotating reflector and a spectroradiometric measurement system is constructed, which simplifies the system’s structural form and enables year-round, full-latitude solar direct radiation spectroscopic measurements without requiring moving tracking. The paper focuses on the study of its optical system, optimizes the design of a polar-axis rotating solar direct radiation spectroscopy measurement optical system with a spectral range of 380–780 nm and a spectral resolution better than 2 nm, and carries out spectral reconstruction of the solar direct radiation spectra as well as the assessment of measurement accuracy. The results show that the point error distribution of the AM0 spectral curve ranges from −9.05% to 13.35%, and the area error distribution ranges from −0.04% to 0.09%; the point error distribution of the AM1.5G spectral curve ranges from −9.19% to 13.66%, and the area error distribution ranges from −0.03% to 0.11%. Both exhibit spatial and temporal uniformity exceeding 99.92%, ensuring excellent measurement performance throughout the year. The measurement method proposed in this study enhances the solar direct radiation spectral measurement system. Compared to the existing dual-axis moving tracking measurement method, the system composition is simplified, enabling direct solar radiation spectrum measurement at all latitudes throughout the year without the need for tracking, providing technical support for the development and application of new technologies for solar direct radiation measurement. It is expected to promote future theoretical research and technological breakthroughs in this field.

Existing stellar map simulators lack color temperature information, have a complex system structure, and cannot independently control the color temperature of stars. Therefore, this study developed an OLED-based semi-physical simulation method and a simulation algorithm for the stellar map with color temperature information to realize a semi-physical simulation of the stellar map close to the real situation in space. The study also aimed to independently control the color temperature of each star. The simulation effect of the stellar map with color temperature information was verified using four stellar maps. The developed simulator achieved independent and controllable color temperature information for each star in the stellar map.

Existing stellar map simulators lack color temperature information, have complex system structures, and cannot independently control the color temperatures of stars. Therefore, this study developed an OLED-based semi-physical simulation method and a simulation algorithm for stellar maps with color temperature information to realize a semi-physical simulation of stellar maps close to the real situation in space. The study also aimed to independently control the color temperature of each star. The simulation effect of the stellar map with color temperature information was verified using four stellar maps. The developed simulator achieved independent and controllable color temperature information for each star in the stellar map.