Abstract
Static thresholding-based or filtering and windowing-based star detection algorithms are the most commonly ones implemented on satellite-onboard star trackers. In this investigation, a new star detection algorithm is proposed. It segments stellar images using an adaptive dynamic thresholding approach, instead of a static one. Therefore, it allows the elimination of the non-uniform random noise in stellar images, and thus facilities and improves the detection of stars in such considered data. Another advantage of the proposed algorithm is that the processing is carried out on-line, which results in a reduced processing time. The performance of the designed algorithm is evaluated by means of established criterion, and obtained results are compared to those achieved by some tested literature approaches. For such a purpose, a set of stellar images is generated. This one deals with numerous types of random noise and different scenarios that can be encountered in space. The obtained results show that the proposed algorithm is more robust, faster and has a better ability to detect stars, compared to the tested literature methods.
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