Tracking and characterizing both active and inactive Space Objects (SOs) is required for
protecting space assets. Characterizing and classifying space debris is critical to …

Abstract The Adaptive Gaussian Mixtures Unscented Kalman Filter (AGMUKF) is introduced
to estimate the attitude of a Resident Space Object using light curves. This filter models the …

Developing a detailed understanding of the Space Object (SO) population is a fundamental
goal of Space Situational Awareness (SSA). The current SO catalog includes simplified …

Over the past few years, Space Situational Awareness (SSA), generally concerned with
acquiring and maintaining knowledge of resident Space Objects (SO) orbiting Earth and …

This paper presents a data driven approach to space object characterisation through the
application of machine learning techniques to observational light curve data. One …

This work presents a data-driven method for the classification of light curve measurements of
Space Objects (SOs) based on a deep learning approach. Here, we design, train, and …

Light curves, or the time-history of photometric brightness, have previously been
demonstrated to allow for estimation of a space object's attitude, shape, and surface …

We describe a new class of deep learning algorithms that can discriminate debris from non-
debris objects using light curve real and simulated data. Recently our team demonstrated …

Current state-of-the-art sensor tasking for space situational awareness is human-analyst
intensive, reactive, and individualized for specific sensors. In application, there are many …

This paper presents a new approach to estimate an observed space object's shape, while
also inferring other attributes, such as its inertial attitude and surface parameters. An …