Dongjin Kim, University of Science and Technology; Kimoon Lee, University of Science and Technology (UST), Korea Aerospace Research Institute (KARI) Campus; Seonho Lee, Korea Aerospace Research Institute; Daewon Chung, KARI
Keywords: Optimization, Imaging mission scheduling, Earth Observation Satellite (EOS), Ground Station
Abstract:
The objective of this paper is to optimize the scheduling of imaging mission for multiple Earth Observation Satellites (EOSs) and ground stations. In response to the increasing user demand for EOS, the number of satellite launches increases exponentially, and accordingly ground stations are being built around the world. Also, the traditional EOS is progressed to Agile Earth Observation Satellite (AEOS) due to the development of satellite attitude control technology. While these offer the advantages such as being able to perform a variety of and many missions in a short time, but in terms of mission planning, it makes a significant challenge due to the complex mission environment. Therefore, optimizing the mission scheduling to efficiently observation targets of high importance with multiple satellites and downlink the raw image date to the ground station is an essential part of the application of the satellite information. In this paper, the mission scheduling problem is divided into two sub-problems as follows: the target observation problem and the downlink problem. The problems are mathematically modelled using Mixed-Integer Linear Programming (MILP). In the target observation problem, the design variable is a single binary variable that is determined whether the target is observed or not. The objective function is to observe as many targets with high priority as given by users, as possible, the constraints include the number of observations, the satellite limitations such as electrical consumption and memory capacity, and the Visible Time Window (VTW) of each target. In the downlinking problem, the task for sending captured images to ground stations, the design variable is the time each satellite communicates with the ground station, and the objective function is to minimize the variance of the remaining memory capacity of the communicating satellites. In other words, rather than downlinking a large amount of data from a specific satellite, the problem modelling is designed to match the memory capacity evenly as much as possible considering the current memory capacity of each satellite. The related constraint is the VTW of each ground station. The modified Dynamic Programming (DP) algorithm which is developed in this study is utilized to solve the target observation problem, and the ground station downlink problem is resolved using the Branch-and-Bound (BB) method. About the mission scenario, it is constructed to demonstrate the feasibility of the proposed approach. First, divide the world region into Northeast Asia, the Middle East, Europe, and the United States. Then, generate the targets randomly within the latitude/longitude range of 20 degrees in each region. a total number of targets are about 400, 100 targets are placed in each region. The parameters of the target such as significance and urgency are also created randomly to reflect the user’s priority. The number of satellites equipped Electro-Optical (EO) camera at Low Earth Orbit (LEO) ranges from 4 to 16, and the 4 Walker Delta constellations are configured, which means 1 to 4 satellites are assigned to each orbit. The planning horizon is 7 days from January 1st to January 8th, 2024. As the expected results of the numerical simulations, the optimized imaging mission plan obtained using the modified DP algorithm can be analyzed in terms of the number of the targets, the satellites, and the mission periods. Additionally, by using the BB method, it is possible to solve the downlink conflict between satellites for each ground station and derive an optimized communication time schedule. Finally, the validity of the modeled problem and the proposed algorithm can be verified through performance comparison with existing algorithms.
Date of Conference: September 19-22, 2023
Track: Space Domain Awareness