Efficient Realistic Conjunction Analysis (EReCA)

The number of artificial objects orbiting the Earth today creates a challenging environment for the protection and safe operation of satellites. With the rise of micro-satellites, nano-satellites, and communications and sensing constellations consisting of hundreds of satellites, the space population is certain to multiply at a robust pace during the next decades of spaceflight, further exacerbating the threat of on-orbit collision posed to active satellites. Soon, the safety of our space assets will require highly accurate and efficient collision prediction.The keys to a collision prediction system that can handle the future growth of data are accuracy in data representation, efficient screening techniques, and automation. Accurate collision risk assessment requires a realistic representation of orbit uncertainty in the calculation. However, accuracy must not come with high computational cost. Smart hardware choices, parallelization, and efficient screening technique are essential to utilize non-Gaussian representation of orbit uncertainties with its computationally-intensive tasks. Automation of the analysis will further improve overall efficiency so that the conjunction assessment process can begin as soon as a warning is received. Stellar Science proposes a novel solution to improve the accuracy and dramatically increase the systematic efficiency of collision probability assessment for objects in Earth orbit. Our proposed system, Efficient and Realistic Conjunction Analysis (EReCA), will leverage recent advances in non-Gaussian orbital uncertainty propagation and existing Monte Carlo techniques for reliable conjunction assessment, as well as current and past work by Stellar Science on the use of spatial data structures to quickly process a large set of data. Our system will be built on a service-oriented approach and will introduce end-to-end automation of the conjunction analysis process to improve efficiency. nbsp;