Satellite constellation method for ground targeting optimized with K-means clustering and genetic algorithm.

This study proposed a satellite constellation method to cluster targets using machine learning and achieve the desired revisit performance for each target in an area of interest. Because a typical satellite constellation, such as the Walker method, does not consider the geometrical dynamics between the satellite and Earth's surface target, the target-oriented revisit performance analysis and the application of optimization method are limited. To overcome these limitations, this study developed a satellite constellation method that is customized according to the target using a repeating ground track orbit and machine learning techniques. The proposed model groups aim to maximize satellite revisit performance via K-means clustering and determine the optimal number of satellites required to achieve the desired revisit performance for each target through a quick and accurate accessibility analysis of various target distributions. This study revealed that three to five target clusters are more effective in improving the satellite revisit performance than a typical single target cluster, while identifying that the density of the target distribution is a significant influencing factor. Finally, the proposed model achieved the desired revisit performance, guaranteeing a timely response for 30 major disaster locations in East Asia and validating the model's optimal performance. [ABSTRACT FROM AUTHOR]