Solving the Agile Earth Observation Satellite Scheduling Problem With Time-Dependent Transition Times

Abstract

The scheduling of Agile Earth Observation Satellites is to select a subset of candidate targets each associated with a profit during their visible time windows in order to maximize the collected profits, under some operational constraints. For each pair of two consecutive observations, a transition time is required to perform a rotating movement of the camera, depending on the start times of the two observations. This time-dependency significantly increases the complexity of the scheduling problem. To solve this problem efficiently, we model the time-dependent transition time and prove that it satisfies the First-In-First-Out rule and the triangle inequalities rule. On this basis, we develop a novel hybrid heuristic, called "Greedy Randomized Iterated Local Search" (GRILS). A specific insert operator including a fast feasibility check and an assignment procedure are specifically designed to address the operational constraints of the scheduling. Extensive experiments on the single satellite instances and multi-satellite instances demonstrate that our algorithm outperforms the state-of-the-art algorithms with respect to solution quality and computation time. Index Terms-time-dependent transition time, satellite scheduling, GRILS heuristic.

Distributed Joint Observation and Transmission Planning Method for Multiple Agile Satellites in Mega Constellation Networks

“…4) The time slot duration τ o and τ c have an integer multiple relationship, so they can be unified with multiple or division operations. For observation missions, the observation duration is generally shorter than the needed attitude transition duration [2], [7], so making τ o be equal to the observation duration is practical. The advantage of diving time domain with τ o is: for each mission, once an observation time slot is determined, the specific observing start and end time is determined.…”

“…As the improvement of EOSs' maneuverability, agile earth observation satellites (AEOSs) are produced. Traditional nonagile EOSs can only observe targets when flying over them, while AEOSs can pitch and roll themselves to observe before or after flying over targets so that more missions can be executed [2]. When an AEOS can observe a target, the AEOS is in the visible time window (VTW) of the target.…”

“…The first aspect focuses on the observation planning of AEOSs, while not considering the offload of the image data. Many research [2], [5], [6] made efforts on the "timedependent transition time", which results from the fact that each pair of consecutive missions requires a transition time to maneuver the look angle of the AEOS from the previous target to the next target. Besides, Han et al [7] studied the cloud coverage uncertainty in the multi-AEOS scheduling problem.…”

“…Motivated by [15] and the development of ISL in MCNs, [15] AND THE PROPOSED MA-DJOTP JOTSAS [15] MA-DJOTP (proposed) we propose a Distributed Joint Observation and Transmission Planning method for Multi-AEOSs (MA-DJOTP) in MCNs. In this paper, we consider the ISLs as laser links and consider the same AEOS AS-01 as in [2], [5], [6], whose look angles are fixed during an observation mission. Comparison of JOTSAS in [15] and the proposed MA-DJOTP is listed in TABLE I.…”

An LEO Constellation Early Warning System Decision-Making Method Based on Hierarchical Reinforcement Learning

“…The local search algorithm showed excellent performance in optimizing the sensor over the process in coherent observations [ 35 ]. Adding local search algorithms to reinforcement learning can further enhance the search capability of the algorithm based on the above advantages.…”