To automate satellite camera control, this chapter introduces the satellite frame selection problem (SFS): find the satellite camera frame parameters that maximize reward during each time window. The authors formalize the SFS problem based on a new reward metric that incorporates both image resolution and coverage. For a set of n client requests and a satellite with m discrete resolution levels, they give an SFS algorithm that computes optimal frame parameters in O(n2.m). For satellites with continuously variable resolution (m = infinite), they give an SFS algorithm that computes optimal frame parameters in O(n3). They have implemented all algorithms and compared computation speeds on randomized input sets. The exact algorithm in this chapter is good for offline applications. However, they are not fast enough to control robotic web cameras, where users demand fast feedback. The computation time should be controlled within a second if possible. Accuracy of the computation is less important if compared with speed requirement. This means the approximation algorithm in next chapter is preferred for such applications.
Exact frame selection algorithms for agile satellites (Chap. 4)
Exakte Bildauswahlalgorithmen für flexible Satelliten (Kap. 4)
2009
29 Seiten, 18 Bilder, 2 Tabellen
Conference paper
English