Evolutionary sensor allocation for the Space Surveillance Network

Abstract

The congested exosphere continues to contain more satellites and debris, raising the potential for destructive collisions. The Special Perturbations (SP) Tasker algorithm currently assigns the ground sensors tasks to track object locations. Accurate locations help avoid collisions. However, the SP Tasker ignores priority, which is the satellite’s importance factor. This article introduces the Evolutionary Algorithm Tasker (EAT) to solve the Satellite Sensor Allocation Problem (SSAP), which is a hybrid Evolutionary Strategy and Genetic Algorithm concept including specific techniques to explore the solution space and exploit the best solutions found. This approach goes beyond the current method, which does not include priority and other methods from the literature that have been applied to small-scale simulations. The SSAP model implementation extends Multi-Objective Evolutionary Algorithms (MOEAs) from the literature while accounting for priorities. Multiple real-world factors are considered, including each sensor’s field-of-view, the orbital opportunities to track a satellite, the capacity of the sensor, and the relative priority of the satellites. The single objective EAT is statistically compared to the SP Tasker algorithm. Simulations show that both the EAT and MOEA approaches effectively use priority in the core tasking algorithms to ensure that higher priority satellites are tracked.