Multi-strip observation scheduling problem for active-imaging agile earth observation satellites

Abstract

Active-imaging agile earth observation satellite (AI-AEOS) is a new generation agile earth observation satellite (AEOS). With renewed capabilities in observation and active imaging, AI-AEOS improves upon the observation capabilities of AEOS and provides 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. A bi-objective optimization model is presented for MSOP along with an adaptive bi-objective memetic algorithm which integrates the combined power of an adaptive large neighborhood search algorithm (ALNS) and a non-dominated sorting genetic algorithm II (NSGA-II). The results of extensive computational 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 provides enhanced capabilities in applied problem solving.