Space Domain Awareness

Space Domain Awareness

Space Domain Awareness

Applying predictive analytics to track and understand the behavior of assets in the space domain

Considering the essential roles satellites play in US defense and intelligence—providing ISR, GPS, and SATCOM—analysts need to know where an object will be in the future, its intent, and what relationships it has to other space objects. Traditional space operations that rely on a ‘detect-track-characterize-catalog’ process can’t keep pace with the hundreds to thousands of additional satellites and smallsats envisioned, along with more debris and other space objects.

As in other domains, Space Domain Awareness (SDA) has become a big data challenge that requires a more computationally robust, automated, and predictive approach.

Probabilistic Satellite Maneuver Prediction: A New Approach to Space Domain Awareness

Aptima’s SDA technologies analyze the activities or ‘patterns of life’ the satellite is engaging in, rather than focusing on the satellite in isolation. Patterns of Life (PoLs) are repeatable, predictable behaviors that satellites exhibit within a certain context and constraints. For example, a satellite’s station-keeping maneuvers form mostly predictable patterns as the satellite re-positions itself to account for orbital perturbations caused by non-uniform gravitational pull, radiation pressure, and atmospheric drag. POLARIS uses an unsupervised machine learning algorithm to analyze these historical patterns, and accurately predict future maneuvers. Critically, from an Indications and Warnings (I&W) perspective, deviations from those expected patterns or maneuvers can be rapidly detected, flagged, and passed to space operators.

Our solutions use multi-source and multi-model data to generate alerts designed for a human operator to inform them of anomalous behavior in resident space objects (RSOs) across three major areas of focus: orbit maneuvers, ground based electro-optical measurements (light curves), and identification of possible unknown RSOs. The system is backed by an internal database to store data, custom machine learning and artificial intelligence algorithms, and a user interface to visualize alerts about analyzed behavior.

This technology has a rich history, where our solutions for satellite maneuver PoL were showcased in 2016, as well as several conferences (see links below for details). More recently our algorithms for unknown satellites and light curve analysis were presented at The Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference in 2021 (articles found here) and at the US led Commercial Sprint Advanced Concept Training (SACT) in 2020 and 2021.

Aptima wishes to thank AFWERX, Air Force Research Laboratory (Space Vehicles Directorate), and Space and Missile Center for the past and continued support to perform research in this cutting-edge area.


For more information, watch the POLARIS video below: