Tamara Payne, Altamira Technologies Corp.; Heath Knickerbocker, Altamira Technologies Corp.; Joshua Niles, Altamira Technologies Corp.; Emily Salisbury, Altamira Technologies Corp.; James Eldridge, Altamira Technologies Corp.; Jordan Wagner, Altamira Technologies Corp.; Douglas Hensley, Altamira Technologies Corp.
Keywords: pattern of life, satellite characterization, photometric signatures, maneuver detection, sensor data fusion
Abstract:
The Space Domain Awareness (SDA) Tools, Applications, and Processing (TAP) Lab is a collaborative environment where industry implements and demonstrates space battle management software for the US Space Force. As part of this effort, the authors have developed algorithms to address three of the Lab’s problem statements. This micro service is called Pattern of Life Analysis Real-time Identification System (POLARIS). It analyzes patterns of life of photometric signatures and astrometric properties of resident space objects. The initial results will be presented for a Geosynchronous Earth Orbit (GEO) satellite. The capability for processing Low Earth Orbit (LEO) orbits is in development. Once the patterns of life are established, statistically significant outliers from the current models are flagged, which generate alert messages containing the details of the change.
POLARIS could contribute to creating decision advantage and avoiding operational surprise in three ways. Firstly, POLARIS generates maneuver patterns of life that provide timing, magnitude, direction of a maneuver sequence, and characteristics of these maneuvers. Secondly, once a new orbit is established, POLARIS automatically predicts proximity events between the maneuvering satellite and the rest of the cataloged satellites. It also describes the details of those encounters. For example, if the orbital trajectories are converging or diverging. Lastly, POLARIS evaluates satellites’ observed behaviors for evidence of camouflage, concealment, deception, or maneuver (CCDM), specifically photometric fluctuations indicating attitude, configuration, and stability changes.
This paper describes the sensor data used as input, the assumptions and methods currently in testing, the processing workflows for orbital patterns of life and for photometric signature patterns of life, and the alert message types containing the automated analysis results. We discuss how we created models for photometry and selected orbital parameters of mean motion, inclination, eccentricity, Right Ascension of the Ascending Node (RAAN), and argument of perigee. Results are shown using two recent real-world examples. Raduga 1-M2 (36358), a Russian communications satellite in geosynchronous orbit transits to graveyard orbit. MEV-1 (44625) that docked with Intelsat 901 (26824) in February of 2020 is shown tugging it to graveyard orbit in March 2025, undocking with it on 4 April 2025. MEV-1 then begins rendezvousing with Optus D3 (35756) on 17 April and docks with it on 26 May 2025. We examine the accuracy and efficacy of the employed methods for detecting changes and false alarm rates. Finally, we discuss future work to improve these results.
Date of Conference: September 16-19, 2025
Track: Satellite Characterization