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A Layered NSGA-II Method for LEO Target Census Constellation Design
Changshou Quan1,2, Ping Jian1,2
1School of Space Information, Space Engineering University, Beijing 101416, China.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
A new layered NSGA-II algorithm optimizes low-Earth orbit (LEO) target census constellations. This method enhances coverage and reduces revisit times, offering an efficient solution for tracking mega-constellations.
Area of Science:
- Space engineering
- Optimization algorithms
- Satellite constellation design
Background:
- Increasing number of low-Earth orbit (LEO) mega-constellations like Starlink presents challenges for space target census.
- Traditional multi-objective optimization algorithms struggle with slow convergence and local optima in high-dimensional spaces.
Purpose of the Study:
- To propose an efficient and applicable method for designing LEO target census constellations.
- To overcome limitations of classical algorithms in optimizing complex constellation parameters.
Main Methods:
- A layered NSGA-II algorithm was developed, dividing decision variables into orbit, configuration, and sensor layers.
- Optimization objectives included coverage rate, revisit period, and constellation cost.
- Performance was evaluated against classical NSGA-II, MOPSO, MOEA/D, and SPEA2 using Starlink satellite ephemeris data.
Main Results:
- The layered NSGA-II achieved a hypervolume of 13.16, outperforming other algorithms.
- A constellation solution provided 99.6% coverage with a 7.6-hour revisit period and a cost of 0.429.
- Convergence speed improved by approximately 36% compared to classical NSGA-II.
Conclusions:
- The proposed layered NSGA-II algorithm offers a superior approach for LEO target census constellation design.
- The method demonstrates significant improvements in coverage, revisit period, and cost-effectiveness.
- This approach is efficient and engineering-applicable for addressing the challenges posed by mega-constellations.
