Low Earth orbit (LEO) constellations are increasingly deployed to provide integrated services such as communication, navigation augmentation, and remote sensing. However, traditional constellation designs—such as Walker, streets‐of‐coverage (SoC), and Flower constellations—rely on distinct parameter sets, making unified optimization of hybrid configurations challenging. Existing unified approaches like the continuous coefficient ( C 2 ) method suffer from high‐dimensional parameter spaces, leading to suboptimal solutions and convergence issues. To address this, we propose an improved continuous coefficient ( i − C 2 ) method that reduces the number of real‐valued parameters while maintaining the ability to represent both symmetric and asymmetric constellation types. Using evolutionary algorithms, we apply the i − C 2 method to design several LEO constellations, including single‐ and dual‐coverage SoC configurations, a navigation‐augmentation hybrid constellation, and an integrated positioning, navigation, timing, remote sensing, and communication (PNTRC) system. Results demonstrate that the i − C 2 method successfully reproduces the Iridium NEXT constellation and identifies “inclination family” structures. Compared to combined orthogonal circular and Walker constellations, the i − C 2 method improves the uniformity of the average number of visible satellites by 20% and 77.92% for 100‐ and 150‐satellite configurations, respectively. This study highlights the i − C 2 method’s capability to enable efficient, flexible, and high‐performance LEO constellation design within a unified optimization framework, offering significant potential for more integrated and cost‐effective satellite systems.
Pan et al. (Thu,) studied this question.