Performance Investigation of the Conjunction Filter Methods and Enhancement of Computation Speed on Conjunction Assessment Analysis with CUDA Techniques

Abstract

The growing number of space objects leads to increases in the potential risks of damage to satellites and generates space debris after colliding. Conjunction assessment analysis is the one of keys to evaluating the collision risk of satellites and satellite operators require the analyzed results as fast as possible to decide and execute collision maneuver planning. However, the computation time to analyze the potential risk of all satellites is proportional to the number of space objects. The conjunction filters and parallel computing techniques can shorten the computation cost of conjunction analysis to provide the analyzed results. Therefore, this paper shows the investigation of the conjunction filter performances (accuracy and computation speed): Smart Sieve, CSieve and CAOS-D (combination of both Smart Sieve and CSieve) in both a single satellite (one vs. all) and all space objects (all vs. all) cases. Then, all the screening filters are developed to implement an algorithm that executes General-purpose computing on graphics processing units (GPGPU) by using NVIDIAs Compute Unified Device Architecture (CUDA). The analyzed results show the comparison results of the accuracy of conjunction screening analysis and computation times of each filter when implemented with the parallel computation techniques.