Optimizing Large-Area Coverage for the LT-4A GEO SAR Satellite: A Hybrid Genetic Algorithm with Adaptive Maneuver Time Control

This paper introduces a computationally efficient mission planning framework tailored for the Ludi Tance-4A (LT-4A), the world's first operational GEO SAR satellite. The LT-4A's unique figure-8 ground track and large-scale imaging capabilities present complex scheduling challenges. Conventional planning methods often rely on fixed maneuver durations, leading to inefficiencies due to the satellite's evolving orbital geometry.

To address this, we propose an Orbital-Window-Aware Hybrid Genetic Algorithm (OW-HGA) integrated with an adaptive maneuver time calculation method. The approach utilizes a two-phase optimization strategy. An offline preprocessing phase exploits geosynchronous periodicity to identify "Optimal Imaging Windows" (OIWs) and dynamically calculates maneuver times based on real-time attitude adjustments. This converts a complex continuous search into a discrete set of opportunities. Subsequently, an online Genetic Algorithm determines the optimal sequence for these OIWs using rapid database lookups rather than time-consuming orbital calculations.

A case study focusing on the Yangtze River Basin validates the proposed method. Comparative results demonstrate a 19.6% reduction in total mission time against greedy algorithms and a 42% reduction in solution variance compared to standard genetic algorithms. Most notably, the framework achieves a 17-fold improvement in computational efficiency. These findings confirm the algorithm’s suitability for real-time mission planning and dynamic replanning for next-generation space-based observation systems.