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Reconfigurable Earth Observation Satellite Scheduling Problem

Brycen D. Pearl, Joseph M. Miller, Hang Woon Lee

TL;DR

This work introduces REOSSP, a reconfigurable Earth Observation Satellite Scheduling Problem that extends the baseline EOSSP by allowing constrained orbital maneuvers to reform a constellation during the planning horizon. The authors formulate a mixed-integer linear program (MILP) to jointly optimize target observations, data downlinks, solar charging, and constellation reconfiguration, while explicitly accounting for onboard data and battery storage and a propellant budget. To address scalability, they develop a rolling horizon procedure (RHP) that decomposes the problem into shorter subproblems with lookahead, achieving substantial runtime improvements with modest loss in optimality. Computational experiments on 24 random instances and a Hurricane Sandy case study show that REOSSP-based schedules significantly outperform the baseline EOSSP in total observations and downlink throughput, with RHP offering a practical trade-off between solution quality and compute time. In summary, constellation reconfigurability coupled with MILP optimization and rolling horizon solving markedly enhances EOS performance for large-scale, realistic mission scenarios.

Abstract

Earth observation satellites (EOSs) play a pivotal role in capturing and analyzing planetary phenomena, ranging from natural disasters to societal development. The EOS scheduling problem (EOSSP), which optimizes the schedule of EOSs, is often solved with respect to nadir-directional EOS systems, thus restricting the observation time of targets and, consequently, the effectiveness of each EOS. This paper leverages state-of-the-art constellation reconfigurability to develop the reconfigurable EOS scheduling problem (REOSSP), wherein EOSs are assumed to be maneuverable, forming a more optimal constellation configuration at multiple opportunities during a schedule. This paper develops a novel mixed-integer linear programming formulation for the REOSSP to optimally solve the scheduling problem for given parameters. Additionally, since the REOSSP can be computationally expensive for large-scale problems, a rolling horizon procedure (RHP) solution method is developed. The performance of the REOSSP is benchmarked against the EOSSP, which serves as a baseline, through a set of random instances where problem characteristics are varied and a case study in which Hurricane Sandy is used to demonstrate realistic performance. These experiments demonstrate the value of constellation reconfigurability in its application to the EOSSP, yielding solutions that improve performance, while the RHP enhances computational runtime for large-scale REOSSP instances.

Reconfigurable Earth Observation Satellite Scheduling Problem

TL;DR

This work introduces REOSSP, a reconfigurable Earth Observation Satellite Scheduling Problem that extends the baseline EOSSP by allowing constrained orbital maneuvers to reform a constellation during the planning horizon. The authors formulate a mixed-integer linear program (MILP) to jointly optimize target observations, data downlinks, solar charging, and constellation reconfiguration, while explicitly accounting for onboard data and battery storage and a propellant budget. To address scalability, they develop a rolling horizon procedure (RHP) that decomposes the problem into shorter subproblems with lookahead, achieving substantial runtime improvements with modest loss in optimality. Computational experiments on 24 random instances and a Hurricane Sandy case study show that REOSSP-based schedules significantly outperform the baseline EOSSP in total observations and downlink throughput, with RHP offering a practical trade-off between solution quality and compute time. In summary, constellation reconfigurability coupled with MILP optimization and rolling horizon solving markedly enhances EOS performance for large-scale, realistic mission scenarios.

Abstract

Earth observation satellites (EOSs) play a pivotal role in capturing and analyzing planetary phenomena, ranging from natural disasters to societal development. The EOS scheduling problem (EOSSP), which optimizes the schedule of EOSs, is often solved with respect to nadir-directional EOS systems, thus restricting the observation time of targets and, consequently, the effectiveness of each EOS. This paper leverages state-of-the-art constellation reconfigurability to develop the reconfigurable EOS scheduling problem (REOSSP), wherein EOSs are assumed to be maneuverable, forming a more optimal constellation configuration at multiple opportunities during a schedule. This paper develops a novel mixed-integer linear programming formulation for the REOSSP to optimally solve the scheduling problem for given parameters. Additionally, since the REOSSP can be computationally expensive for large-scale problems, a rolling horizon procedure (RHP) solution method is developed. The performance of the REOSSP is benchmarked against the EOSSP, which serves as a baseline, through a set of random instances where problem characteristics are varied and a case study in which Hurricane Sandy is used to demonstrate realistic performance. These experiments demonstrate the value of constellation reconfigurability in its application to the EOSSP, yielding solutions that improve performance, while the RHP enhances computational runtime for large-scale REOSSP instances.

Paper Structure

This paper contains 37 sections, 26 equations, 10 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Earth Observation Satellite Scheduling Problem---Baseline
  3. Decision Variables and Indicator Variables
  4. Objective Function
  5. Constraints
  6. Time Window Constraints
  7. Data Tracking and Storage Constraints
  8. Battery Tracking and Storage Constraints
  9. Full Formulation
  10. Reconfigurable Earth Observation Satellite Scheduling Problem
  11. Decision Variables
  12. Objective Function
  13. Constraints
  14. Orbital Maneuver Path Continuity Constraints
  15. Time Window Constraints
  16. ...and 22 more sections

Figures (10)

  • Figure 1: A demonstration of the capabilities enabled in the REOSSP.
  • Figure 2: Feasible observation, downlink, and solar charging occurrence.
  • Figure 3: Data and battery storage level visualizations of satellite $k$.
  • Figure 4: Feasible orbital maneuver progression for $K$ satellites.
  • Figure 5: Results of the random instances.
  • ...and 5 more figures