Automatic Orbit Selection for a Radio Interferometric Spacecraft Constellation

Abstract

A constellation of radio telescope spacecraft can leverage interferometry to accurately image distant objects throughout the universe, but mission design must balance among many interrelated constraints. In particular, the number of spacecraft and their time-varying orbital parameters determine what interferometric baselines are feasible for each target, which in turn drives the imaging capabilities of the constellation. The large combinatorics of dynamic constellation configuration and the numerous competing engineering concerns present a challenge that is not well addressed by labor-intensive manual mission design processes. This paper describes search-based optimization methods that direct mission design effort toward promising constellation geometries: those that achieve broad interferometric coverage but remain cost-effective and resilient to failures. Six families of automatic optimization algorithms with complementary search strategies were created to explore among explicit constellation configuration plans. Evaluation of each candidate constellation plan was accelerated by efficiently combining precomputed caches of orbital and interferometric data. Comparative results indicate that leveraging automated optimization for constellation mission design is practical and useful. Optimized constellations demonstrated target image reconstruction errors 10% better than a manually designed constellation and up to 35% better than random solutions.