LEO-NA Walker Constellation Design with Bi-objective Optimisation Approaches
For satellite navigation system designers, this work provides a practical optimization method to improve LEO constellation performance under cost constraints.
This paper proposes a bi-objective optimization framework for LEO Walker constellation design that balances navigation performance and deployment cost, achieving a 42.5% and 24.4% improvement in average visible satellites and an 18.9% and 10.5% reduction in mean PDOP compared to Polar and optimized-LFC constellations, respectively.
Low Earth Orbit (LEO) constellation design for navigation augmentation (NA) has attracted increasing attention in navigation satellite system studies, yet balancing navigation performance and deployment cost remains a fundamental challenge. To address this issue, this paper proposes a bi-objective optimization framework for LEO Walker constellation design. The problem is formulated as a bi-objective optimization model with constellation cost and positioning accuracy as objectives. In the formulation, PDOP tail risk and satellite visibility are incorporated into the objective formulation to better characterize navigation performance. The Pareto-optimal solution set isobtained using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Simulation results show that, under the same satellite deployment cost, the proposed LEO-NA Walker constellation improves the average number of visible satellites by 42.5% and 24.4%, and reduces the mean PDOP by 18.9% and 10.5% compared with representative Polar and optimized-LFC constellations, respectively, thereby enhancing service continuity and resource utilization efficiency. These results provide useful guidance for the design and deployment of LEO-NA constellations.