LAUSR

A unified description for the imaging mission of different types of targets is lacking, making overall optimization of imaging missions of complex multitype targets (point, curve, and area) within a single pass difficult when using traditional satellite imaging mission planning. We propose an imaging mission planning modeling method based on the optimal mission decomposition/merge (MD/M) strategy for imaging missions of multitype targets within a single pass of SA-EOSs. This method transforms the imaging missions of multitype targets into an atomic mission set that can be described uniformly for integrated optimization scheduling. First, an optimal MD/M strategy was proposed for different types of target imaging (point, curve, and area) based on the characteristics of dynamic imaging. A mission optimization model was then constructed, with imaging coverage benefit and mission execution time as the objective functions, and an improved particle swarm optimization algorithm was used to solve the model. Finally, the proposed optimal MD/M strategy and mission planning modeling method were tested by setting up seven groups of imaging mission simulation experiments with different multi-type target combinations. The result showed that the proposed “two-stage” optimization method achieved integrated optimization of multimode imaging missions in dynamic imaging. The proposed optimal MD/M strategy can be applied in integrated modeling of imaging missions of multitype targets. Moreover, the imaging mission planning model constructed with time-stamped strips as atomic tasks can perform efficient integrated planning of imaging mission of complex multitype targets and ensures the effective performance of SA-EOSs.