Denoising star map data via sparse representation and dictionary learning

As the highest precision devices of celestial navigation system [1], star sensors have been getting more and more attention in recent years. In which the star image positioning and recognition is the key technology of CNS, while the extraction of stars from star maps is the first step. By the background noise, there are some error extractions when traditional methods are used, which can even lead to the failure of star map matching. To solve this problem, a denoising method based on overcomplete sparse representation is presented in this paper. This method uses the adaptive sparse decomposition of star map in the redundant dictionary to process the threshold, as a result, the reliability of star extraction is improved. The experimental results show that the correct rate of this method that extracting star after reducing background noise of star map is close to 100%.