Orbital Design Optimization for Large-Scale SAR Constellations: A Hybrid Framework Integrating Fuzzy Rules and Chaotic Sequences

Abstract

Synthetic Aperture Radar (SAR) constellations have become a key technology for disaster monitoring, terrain mapping, and ocean surveillance due to their all-weather and high-resolution imaging capabilities. However, the design of large-scale SAR constellations faces multi-objective optimization challenges, including short revisit cycles, wide coverage, high-performance imaging, and cost-effectiveness. Traditional optimization methods, such as genetic algorithms, suffer from issues like parameter dependency, slow convergence, and the complexity of multi-objective trade-offs. To address these challenges, this paper proposes a hybrid optimization framework that integrates chaotic sequence initialization and fuzzy rule-based decision mechanisms to solve high-dimensional constellation design problems. The framework generates the initial population using chaotic mapping, adaptively adjusts crossover strategies through fuzzy logic, and achieves multi-objective optimization via a weighted objective function. The simulation results demonstrate that the proposed method outperforms traditional algorithms in optimization performance, convergence speed, and robustness. Specifically, the average fitness value of the proposed method across 20 independent runs improved by 40.47% and 35.48% compared to roulette wheel selection and tournament selection, respectively. Furthermore, parameter sensitivity analysis and robustness experiments confirm the stability and superiority of the proposed method under varying parameter configurations. This study provides an efficient and reliable solution for the orbital design of large-scale SAR constellations, offering significant engineering application value.