Dynamic Task Planning Method for Multi-Source Remote Sensing Satellite Cooperative Observation in Complex Scenarios

Abstract

As the number and variety of remote sensing satellites continue to grow, user demands are becoming increasingly complex and diverse. Concurrently, there is an escalating requirement for timeliness in satellite observations, thereby augmenting the complexity of task processing and resource allocation. In response to these challenges, this paper proposes an innovative method for dynamic task planning in multi-source remote sensing satellite cooperative observations tailored to complex scenarios. In the task processing phase, this study develops a preprocessing model suitable for various types of targets, enabling the decomposition of complex scenes into multiple point targets for independent satellite observation, thereby reducing the complexity of the problem. In the resource allocation phase, a dynamic task planning algorithm for multi-satellite cooperative observation is designed to achieve dynamic and optimized scheduling of the processed point targets, catering to the needs of multi-source remote sensing satellites. Empirical validation demonstrated that this method effectively implements dynamic adjustment plans for point targets, comprehensively optimizing the number of observation targets, computation time, task priority, and satellite resource utilization, significantly enhancing the dynamic observation efficiency of remote sensing satellites.

Dynamic Merging for Optimal Onboard Resource Utilization: Innovating Mission Queue Constructing Method in Multi-Satellite Spatial Information Networks

“…At present, most research on multi-satellite mission scheduling is based on the dynamic programming algorithm, greedy algorithm, and heuristic algorithm [18][19][20][21]. In recent years, the exploration of multi-satellite mission scheduling has made continuous progress.…”

“…In recent years, the exploration of multi-satellite mission scheduling has made continuous progress. Wu et al [20], by designing preprocessing models for different types of targets to decompose complex scenes, and using dynamic task planning algorithms to optimize the resource allocation for multi-satellite cooperative observation, effectively improved the efficiency of dynamic observation by remote sensing satellites. For the dynamic area task scheduling problem, Yin and Clerckx [18] proposed an emergency response satellite scheduling model, which, using dynamic segmentation algorithm and dynamic Greedy state algorithm, can intelligently assign satellite imaging tasks and has a good application prospect.…”