Target clustering method of imaging satellite observation scheduling
A technology of target clustering and imaging satellites, which is applied in the field of aerospace applications, can solve problems such as insignificant effects, low observation efficiency, and poor operability, and achieve the effects of saving calculation time, narrowing the search range, and avoiding repeated calculations
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Embodiment 1
[0058] Embodiment 1: as figure 1 , figure 2 and image 3 As shown, according to the satellite imaging mechanism, it can be seen that the satellite’s spaceborne remote sensor (camera) can cover a horizontal observation strip on the ground at one time. The width of the strip is the width of the satellite’s field of view, which is defined by the In the radial direction, the length of the observation swath is determined by the radial flight distance of the satellite during the duration of the observation activity. The observable area of the satellite is usually a symmetrical area with the sub-satellite point track line as the center line. The boundary width of the symmetrical area is determined by the maximum roll angle of the satellite, and the single observation area of the satellite is determined by the satellite's on-board It is determined by the field of view of the remote sensor. The roll angle used by the satellite to observe the target is defined as the observation...
Embodiment 2
[0169]Embodiment 2: The principle and steps of the target clustering method for imaging satellite observation scheduling in this embodiment are basically the same as those in Embodiment 1. The only difference is that this embodiment omits the calculation of the best observation angle for observation activities.
[0170] Such as Figure 7 As shown, it is assumed that the maximum number of side swings in a single orbit of a satellite is 2 times, and there are 4 observation targets in total, that is, the satellite needs to cover the maximum number of targets within 2 side swing observation opportunities. Figure 7 (b) is a schematic diagram of all possible task clustering methods between tasks. In order to obtain the maximum coverage scheme, a dynamic programming method is used to solve the maximum coverage model as in Example 1.
[0171] let f k (T i ) for T i is the maximum income of the k-th clustering task of the starting task node, d(T i ,T l ) are the start and end tas...
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