In near future, commercial companies have proposed to launch the extreme large constellation of small to medium-sized satellites over new 20,000 satellites in orbit. As a result, satellite operators will face a new challenge and have to prepare some solutions to mitigate huge potential collision risks. Under current practices, operators receive collision warning messages (Conjunction Data Messages: CDMs) generated by the Combined Space Operations Center (CSpOC). It is possibly to receive huge conjunctions that exceed warning threshold control each day but many of them are false conjunctions because the result of a combination of inaccuracies in the sensor measurements, predicting error and especially inadequacy of satellite information (e.g., GPS data and satellite size) To reduce the false CDMs and effectively mitigate the potential risk in time, space traffic management system can minimize the problems. The key system capabilities are able to automatically screen potential risky objects and provide an analysis tool to decide effective maneuver planning. As a result, GISTDA initiates the project to develop the space traffic management system known as “ZIRCON” to monitor and mitigate the risk of GISTDA’s satellites and future satellites (e.g., THEOS2 project). This paper presents the operational collision avoidance process and algorithm analysis of space traffic management system. The accuracy of analysis results is validated by comparing the analyzed results of CSpOC and Space Data Association (SDA) serving the commercial space sector to monitor and provide warning message for membership. The last section describes the future development planning and services for satellite operators.

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.