A Hybrid Method for Dense Points Enclosing and Observation Planning

As a new form of Earth-observation requirement, dense points usually contain a large number of points of interest (Pols) which are distributed in a wide area and need to be planed by involving several satellites to perform the tasks collaboratively. This challenges the usage of satellites when visible time window is calculated and decided separately for each Pol, causing frequent switching among a lot of Pol captures. While traditional methods are more suitable for observing fewer Pols, denser points require a different approach to fully utilize satellite resources and avoid inefficient tasking. Meanwhile, because Pols are geographically clustered and dispersed in different regions, more satellites need to be analyzed in order to generate a collaborative planning result. This paper proposed a dense-point aggregation method that uses clustering to generate a hybrid task representation based on the geographic distribution of Pols. A collaborative plan modeling and solving algorithm is proposed to provide a compatible optimization with this hybrid representation. The efficacy of the proposed method is demonstrated in the experiment to show the advantage of the proposed method.