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Implicit Q-learning-bootstrapped ant colony optimization for maritime moving-target observation scheduling with agile satellites

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Maritime moving-target observation scheduling with agile Earth observation satellites is a dynamic, sequence-dependent combinatorial optimization problem. Sea-surface targets move continuously, causing feasible observation windows to vary with target motion and satellite orbital geometry. The scheduler must jointly determine task selection, satellite assignment, observation-window selection, and observation ordering under time-window, attitude-maneuvering, onboard-resource, and cloud-affected availability constraints. This paper proposes an implicit Q-learning-bootstrapped ant colony optimizat

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