Optimization of Reconfigurable Satellite Constellations Using Simulated Annealing and Genetic Algorithm.

Area of Science:

• Aerospace Engineering
• Geospatial Science
• Systems Engineering

Background:

• Agile Earth observation requires responsive satellite capabilities like rapid launches and orbit adjustments.
• Existing satellite constellations often lack the flexibility for dynamic mission requirements, such as disaster monitoring.

Purpose of the Study:

• To develop a framework for designing reconfigurable satellite constellations (ReCon) for both global and regional Earth observation modes.
• To co-optimize satellite design and orbital parameters using systems engineering approaches.

Main Methods:

• Employed systems engineering for a multidisciplinary optimization of satellite design and orbits.
• Utilized simulated annealing (SA) and genetic algorithm (GA) as heuristic methods, benchmarking against a gradient-based approach.
• Evaluated the performance and feasibility of the ReCon design.

Main Results:

• Population-based genetic algorithm (GA) outperformed simulated annealing (SA) and gradient-based methods.
• The designed ReCon satellite constellation is physically feasible.
• ReCon constellations demonstrate a superior performance-to-cost ratio compared to static constellations.

Conclusions:

• The ReCon framework enables agile Earth observation, adaptable for regular and disaster monitoring.
• The developed design is a feasible and cost-effective alternative to static satellite constellations.
• Future work will extend ReCon capabilities to diverse observation types and wavelengths.