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.

In high-precision celestial navigation, star map recognition algorithms are crucial. We identified limitations in the classical grid star map recognition algorithm (CGSMRA) concerning star sorting method, selection strategy, scoring criterion, and screening mechanisms. To address these, we developed a multidimensional optimization-improved grid star map recognition algorithm (MOIGSMRA). We evaluated MOIGSMRA through five experiments: template matching efficiency, companion star recognition, recognition accuracy, attitude determination accuracy, and overall performance. Compared to CGSMRA, MOIGSMRA demonstrated superior results. This study offers a method to optimize attitude determination algorithms for star sensors and provides a theoretical and experimental foundation for improving star recognition accuracy.

Accurate measurements of direct solar radiation spectra are crucial for atmospheric science, climatology, agriculture, and solar energy. Existing systems depend on costly dual-axis tracking devices, leading to high maintenance and error rates. This study presents a free-form surface-based polar-axis rotating solar direct radiation spectrometer, enabling year-round measurements across all latitudes without mobile tracking. The system operates in the 380–780 nm range with a spectral resolution better than 2 nm. Simulation results demonstrate spectral curve area errors between 0.68% and 1.22%, and outdoor experiments in Changchun, China, confirm the accuracy of measurements against the AM1.5 G standard.

To realize year-round direct solar-radiation intensity measurement at all latitudes without mobile tracking, this study proposes a measurement method based on multiple off-axis hyperboloids fused with free-form surfaces and a new system architecture composed of direct solar-radiation measurement mirrors and pyroelectric sensors. Simulations showed the irradiance uniformity to be 97.95% and spot energy distribution uniformity to be 91.92% to 94.84%, which are better than those of similar methods in the international field of view. Performance tests also confirmed the correctness and feasibility of the proposed method. This study provides an improved theory and system design for direct solar-radiation intensity measurement.

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.

In this study, we proposed a method for reproducing the meteorological optical range (MOR) definition using array optical components and constructed a system architecture. Thirty light-emitting diodes were selected via a dual evaluation system for spectral homogeneity and purity. Combined with an optimized optical architecture—including dual free-form lenses and a microlens array—and a long short-term memory–non-dominated sorting genetic algorithm II—the system achieved 96% irradiance uniformity over the 65 × 65-mm range, < 3.6° beam divergence, and a spectral matching error of -4.24%. This approach enables accurate MOR definition reproduction, enhancing traceability and supporting the calibration of visibility measurements.