A Layered NSGA-II Method for LEO Target Census Constellation Design

To address the pressure of space target census posed by low-orbit mega-constellations such as Starlink, this paper proposes an LEO target census constellation design method based on a layered NSGA-II algorithm. Six decision variables—orbital altitude, inclination, number of orbital planes, number of satellites per plane, sensor field of view, and focal length—are considered. Coverage rate, revisit period, and constellation cost are taken as optimization objectives to establish a multi-objective optimization model. To overcome the issues of slow convergence and susceptibility to local optima in high-dimensional decision spaces faced by classical multi-objective optimization algorithms, the decision variables are divided into the orbit layer, configuration layer, and sensor layer. NSGA-II evolution is performed layer by layer, with elite retention and global archive passing of high-quality solutions. Using high-precision 24-h ephemeris of 496 Starlink satellites generated by STK as the simulation object, the proposed layered NSGA-II is compared with classical NSGA-II, MOPSO, MOEA/D, and SPEA2. Results show that the layered NSGA-II achieves a hypervolume (HV) of 13.16, outperforming the other algorithms. Under the condition of maintaining 99.6% coverage, the recommended constellation solution achieves a revisit period as low as 7.6 h and a constellation cost of 0.429, demonstrating significantly better comprehensive performance than other algorithms. Convergence speed is improved by approximately 36% compared to classical NSGA-II. This method provides an efficient and engineering-applicable optimization approach for LEO target census constellation design.