Satellite Collision Detection using Spatial Data Structures

In recent years, the number of artificial objects in Earth orbit has increased rapidly due to lower launch costs and new applications for satellites. More and more governments and private companies are discovering space for their own purposes. Private companies are using space as a new business field, launching thousands of satellites into orbit to offer services like worldwide Internet access. Consequently, the probability of collisions and, thus, the degradation of the orbital environment is rapidly increasing. To avoid devastating collisions at an early stage, efficient algorithms are required to identify satellites approaching each other. Traditional deterministic filter-based conjunction detection algorithms compare each satellite to every other satellite and pass them through a chain of orbital filters. Unfortunately, this leads to a runtime complexity of O(n2). In this paper, we propose two alternative approaches that rely on spatial data structures and thus allow us to exploit modern hardware’s parallelism efficiently. Firstly, we introduce a purely grid-based variant that relies on non-blocking atomic hash maps to identify conjunctions. Secondly, we present a hybrid method that combines this approach with traditional filter chains. Both implementations make it possible to identify conjunctions in a large population with millions of satellites with high precision in a comparatively short time. While the grid-based variant is characterized by lower memory consumption, the hybrid variant is faster if enough memory is available.