Conjunction Time and Collision Probability Calculation Based on Bayesian Optimization

摘要

Collision probability calculation is critical to space situational awareness or space traffic management. To determine the collision probability, the conjunction time is required first. In this paper, the continuous representation of the polynomial chaos is used to describe the uncertainty of space objects along with time. Based on this representation, the Bayesian optimization is employed to find the conjunction time and then the collision probability. The polynomial chaos representation and Bayesian optimization do not require Gaussian assumption and are computationally more efficient than the Monte Carlo sampling-based method to calculate collision probability and time. The effect of the order of polynomial chaos as well as the sampling period is also analyzed. Two numerical experiments are used to show the effectiveness of the proposed algorithm.