Integrated Imaging Mission Planning Modeling Method for Multi-Type Targets for Super-Agile Earth Observation Satellite

Abstract

A unified description for the imaging mission of different types of targets is lacking, making overall optimization of imaging missions of complex multi-type 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 multi-type targets within a single pass of super-agile earth observation satellites. This method transforms the imaging missions of multi-type 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 "twostage" optimization method achieved integrated optimization of multi-mode imaging missions in dynamic imaging. The proposed optimal MD/M strategy can be applied in integrated modeling of imaging missions of multi-type 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 multi-type targets and ensures the effective performance of super-agile earth observation satellites.

A Multiple Agile Satellite Staring Observation Mission Planning Method for Dense Regions

“…Long [21] implemented a two-phase process of mission clustering and planning, utilizing an improved graph-theoretic cluster partitioning algorithm along with a hybrid genetic and simulated annealing (GA-SA) algorithm to augment its optimization capability. Lu [22] presented a unified description for different types of targets and utilized an improved particle swarm algorithm to solve the mission planning model. However, these studies are only applicable to planning and scheduling for a single satellite and cannot manage large-scale mission planning problems.…”

Large-scale mission scheduling for multiple heterogeneous agile earth observation satellites using modified chameleon swarm algorithm

“…Although many metaheuristic algorithms have already been developed to solve the mission scheduling problem for AEOSs, there are still some challenges to overcome [39][40][41][42]. The main challenge comes from the low computational efficiency, which can be explained from two perspectives.…”