Density-based in-orbit collision risk model extension to any impact geometry

TL;DR

This paper extends density-based collision risk models to accommodate any impact geometry and orbital dynamics, enabling efficient and accurate assessment of collision risks from space debris in complex scenarios.

Contribution

It introduces a generalized probabilistic model that transforms phase space densities into spatial densities for diverse impact geometries and orbital conditions.

Findings

Model accurately estimates impact flux from fragmentation clouds.

Application to real debris clouds demonstrates improved risk assessment.

Discusses impact of model features on collision rate predictions.

Abstract

Neglecting small fragments in space debris evolutionary models can lead to a significant underestimation of the collision risk for operational satellites. However, when scaling down to the millimeter range, the debris population grows to over one million objects, making deterministic approaches too computationally expensive. On the contrary, probabilistic models provide a more efficient alternative, which however typically work under some simplifying assumptions on the dynamics, limiting their field of applicability. This work proposes an extension of the density-based collision risk models to any orbital dynamics and impact geometry. The impact rate with a target satellite is derived from a multi-dimensional phase space density function in Keplerian elements, which discretely varies over both phase space and time. The assumption of a bin-wise constant cloud density allows for the analytical transformation of the six-dimensional distribution in orbital elements into the three-dimensional spatial density function, guaranteeing an efficient and accurate evaluation of the fragments flux. The proposed method is applied to the assessment of the collision risk posed by real fragmentation clouds in different orbital regions on a high-risk target object. The effect on the impact rate of the additional model features, compared to previous probabilistic formulations, is discussed.