A Star Spot Extraction Method Based on SSA-UNet for Star Sensors Under Dynamic Conditions

Dynamic performance is an important index of star sensors. When the star sensor is under dynamic conditions, imaged star spots stretch to steaks as the energy is more dispersive than that of static conditions, which leads to a decrease in the signal-to-noise ratio (SNR) of the star spots. The threshold method is unable to detect enough stars under dynamic conditions. However, star sensors need sufficient stars to guarantee normal work. The image enhancement method enhances the star spots by integrating along with their movement directions. However, it still misses a lot of low-SNR stars as the determination of movement directions is sensitive to noise, which leads to the limited enhancement of low-SNR stars. To solve the problem, this study presented an improved UNet-based star spot extraction method for star sensors under dynamic conditions. The improved UNet, defined as SSA-UNet, used a star streak attention module (SSAM) to help it focus on the star spots and improve the extraction performance. The presented method used SSA-UNet for the rough extraction and modified the rough extraction in the subsequent process. Both simulation and ground experiments proved that the proposed method can accurately extract more low-SNR star spots than the image enhancement method, which had great significance for further improving the dynamic performance of star sensors.