Self-adaptive Planning Method of Imaging Reconnaissance Satellites Area Coverage

Area coverage using imaging reconnaissance satellites belongs to mission planning problems constrained by spatio-temporal information. The current area coverage algorithms can be divided into two categories:based on single-satellite and multi-satellite. The former cannot fully utilize satellite resources, and is replaced by the latter gradually. However, the latter usually applies the existing intelligent optimization approaches simply. In addition, the drawback of both is heavily depending on user's intervene. Therefore, in this paper, a self-adaptive approach is proposed for the low-effective and time-consuming issue of current covering algorithms. Firstly, a self-adaptive way for grid division is presented to automatically generate grid; secondly, in order to balance imaging accuracy and efficiency, the largest visual coverage computing is proposed to determine the angle of each satellite; thirdly, a semantic-based sliding window optimizing strategy is designed to calculate planning sequence for area coverage. Compared with the classic algorithms, this method reduces human-computer interaction, and is more effective and robust. It has already been applied in area coverage of real conditions.