Space Situational Awareness, a new challenge for tracking and orbital dynamics

Space Situational Awareness (SSA) aims at avoiding collisions: collisions between objects on orbit with one another, but also between objects and the Earth, where the objects are big enough to cause significant local or global damage. Avoiding on-orbit collisions is not only relevant in order to protect active space assets, but also to avoid cascading effects and to ensure the sustainable use of the near Earth space environment in the future.
One of the challenges of SSA is the multi-target tracking of the numerous space objects, which requires the association of disconnected observation sets. Standard methods based on Bayesian approaches, finite set statistics, or admissible regions all rely on a realistic representation of the covariance (more generally, uncertainty) and that quantities, such as the probability of detection, can be realistically and accurately defined. The talk demonstrates the shortcomings of current approaches and offers non-Keplerian extensions via efficient coupled orbit-attitude propagation including orbital perturbations, leading to a first artificial space object taxonomy. This allows for accurate orbit tracking and prediction for collision avoidance.