Multi-strip observation scheduling problem for ac-tive-imaging agile earth observation satellites
This addresses the complex scheduling challenge for satellite operators, but it is incremental as it builds on existing optimization techniques.
The paper tackles the multi-strip observation scheduling problem for active-imaging agile earth observation satellites by presenting a bi-objective optimization model and an adaptive memetic algorithm combining ALNS and NSGA-II, resulting in superior outcomes compared to existing methods.
Active-imaging agile earth observation satellite (AI-AEOS) is a new generation agile earth observation satellite (AEOS). With renewed capabilities in observation and active im-aging, AI-AEOS improves upon the observation capabilities of AEOS and provide additional ways to observe ground targets. This however makes the observation scheduling problem for these agile earth observation satellite more complex, especially when considering multi-strip ground targets. In this paper, we investigate the multi-strip observation scheduling problem for an active-image agile earth observation satellite (MOSP). A bi-objective optimization model is presented for MOSP along with an adaptive bi-objective memetic algorithm which integrates the combined power of an adaptive large neighborhood search algorithm (ALNS) and a nondominated sorting genetic algorithm II (NSGA-II). Results of extensive computa-tional experiments are presented which disclose that ALNS and NSGA-II when worked in unison produced superior outcomes. Our model is more versatile than existing models and provide enhanced capabilities in applied problem solving.