TWO-STEP OPTIMIZATION STRATEGY FOR AUTOMATED SCHEDULING AND TASKING OF SPACE-BASED SSA SENSORS

The growing population of Resident Space Objects (RSOs) in the near-Earth environment demands increasingly sophisticated sensor tasking strategies capable of maintaining continuous catalogue coverage under tight observational constraints. This paper presents a two-stage pipeline for automated scheduling of space-based Space Situational Awareness (SBSSA) sensors, designed to handle the combinatorial complexity arising from overlapping observation windows in congested orbital scenarios. In the first step, a cluster-based conflict resolution stage processes the output of a constraint-aware visibility window extraction and systematically generates all feasible, non-overlapping observation candidates, providing a complete representation of the admissible tasking space without prematurely discarding any valid configuration. In the second step, a Particle Swarm Optimization (PSO) scheduler operates on this conflict-free candidate set through a multi-objective fitness function balancing, among the others, gap minimization, target prioritization, and geometric diversity for multi-sensor triangulation. Performance is evaluated in a dedicated simulation environment against greedy baseline strategies, demonstrating improved global coverage and priority-weighted observation continuity across representative orbital scenarios.