Technical Papers – Machine Learning for SDA Applications

A Modular Benchmarking Framework for Evaluating Large Language Models in Space Situational Awareness using Notice to Space Operators Data (2025)
Trier Mortlock, Lawrence Livermore National Laboratory; Ronit Agarwala, Lawrence Livermore National Laboratory; Jayson Luc Peterson, Lawrence Livermore National Laboratory; Imene Goumiri, Lawrence Livermore National Laboratory; Jason Bernstein, Lawrence Livermore National Laboratory

Keywords: Natural Language Processing, Large Language Models, Space Domain Awareness, Notice to Space Operators

Agentic LLMs & Multimodal Data Fusion for Space Domain Awareness (2025)
Karis Courey, Booz Allen Hamilton; Stephen Gerrells, Booz Allen Hamilton; Michael Moniger, Booz Allen Hamilton; Aubrey Shields, Booz Allen Hamilton; Karen Phumisithikul, Booz Allen Hamilton

Keywords: LLMs, Space Domain Awareness, SDA, Space Battle Management, SBM, Multi-Agent Systems, Agentic Architectures, Orbital Data Analysis, Publicly Available Information, PAI, Multimodal Data Fusion, AI, Machine Learning, Anomaly Detection, RAG, NLP

AI-Driven Satellite Characterization: Enhancing RSO Detection with Deep Learning and Non-Earth Imaging (2025)
Alexander Rogers, Turion Space; Mohamed Hasan, Turion Space; Ryan Westerdahl, Turion Space; Thomas Cooley, Turion Space

Keywords: Machine Learning, space-based imaging, non-earth imaging, uncertainty quantification, trustworthy AI

Applying Deep Learning Object Detection Techniques to Detect RSOs for Ground-Based EO Sensors  (2025)
Aidan Lorenz, ARKA; Shawn Abernethy, ARKA; Mike Fischer, ARKA; Jacob Griesbach, ARKA; W. Jody Mandeville, InTrack Radar Technologies; Sid Arora, InTrack Radar Technologies; Tim McLaughlin, InTrack Radar Technologies; Harshitha Challa, West Virginia University; Piyush Mehta, West Virginia University

Keywords: machine learning, object detection, convolutional neural networks, CNNs, LNT, debris detection

Applying Deep Learning to Anomaly Detection of Russian Satellite Activity for Indications Prior to Military Activity (2025)
David Kurtenbach, Kansas State University; Megan Manly, Kansas State University; Zach Metzinger, Kansas State University

Keywords: Autoencoder, Russia, Pattern of Life, Anomaly Detection, Deep Learning, SDA

Automated Target Injection for Sensor-Specific Model Calibration (2025)
Kevin Phan, EO Solutions; David Chaparro, EO Solutions; Taylor Phan, EO Solutions; Justin Fletcher, Space Systems Command A&AS

Keywords: Space Domain Awareness, Sensor Drift, Automated Target Injection, Space Situational Awareness, Object Detection, Transfer Learning, Small Aperture Telescope, Machine Learning, Deep Learning

Cosmic Collaboratory: The SDA AI/ML Model Hosting Service (2025)
John Ossorgin, Sandia National Laboratories; Forest Danford, Sandia National Laboratories; Kyle Merry, Sandia National Laboratories

Keywords: model zoo, hosting service, AI/ML, models, SDA, SSA, community, space control

Electro-optical Signature Generation Using Physic-Aided Deep Neural Networks (2025)
Jorge O'Farrill, Modern Technology Solutions Incorporated; Nathan Highsmith, Modern Technology Solutions Incorporated; Isaac Nelson, University of Alabama; Elise Theriot, University of Alabama

Keywords: AI/ML, attention, transformers

Genetic Algorithm-Driven Scheduling for Radar-Based Satellite Tracking (2025)
Kenny Andersson, Swedish Defence Research Agency; Tim Kinnunen, Swedish Defence Research Agency; Semeli Papadogiannakis, Swedish Defence Research Agency; Rolf Ragnarsson, Swedish Defence Research Agency

Keywords: Space situational awareness, Sensor planning, Optimization, Radar, Sensor resource allocation

Hierarchical Neuro-Symbolic AI for Autonomous Spacecraft Maneuvering and Anomaly Detection (2025)
Allan Grosvenor, MSBAI; Abdul Wahab, MSBAI; Kyrylo Bohachov, MSBAI; Anton Zemlyansky, MSBAI; Ryland Adams, MSBAI; Dwyer Deighan, MSBAI

Keywords: autonomous systems, spacecraft control, anomaly detection, hierarchical AI, neuro-symbolic architecture, reinforcement learning, space situational awareness, space domain awareness, maneuver prediction, space traffic management

Machine Learning for Event-Based Vision Sensor Space Domain Awareness Object Tracking (2025)
Rachel Oliver, Air Force Institute of Technology; Michael Albert, University of Texas El Paso; Olac Fuentes, The University of Texas at El Paso; Dmitry Savransky, Cornell University

Keywords: Artificial Intelligence, Machine Learning, Space Domain Awareness, Object Tracking, Event-based Vision Sensors, Space-based

Operational Photometry for SDA: Robust, Source-Agnostic, and Sim-to-Real Ready (2025)
Kimmy De Alba, Space Systems Command A&AS; Kevin Phan, EO Solutions; Alex Cabello, EO Solutions; Zach Gazak, Space Systems Command A&AS; Justin Fletcher, Space Systems Command A&AS

Keywords: Aperture Photometry, Machine Learning, Satellites, Stars, Autonomous Telescope Networks, Light Curves, Anomaly Detection

Rapid Deployment, Calibration, and Training of Optical Observatories for Space Domain Awareness (2025)
Zach Gazak, Space Systems Command A&AS; Ryan Swindle, Space Systems Command A&AS; Sierra Morales, University of Hawaii at Manoa; Matthew Phelps, Space Systems Command A&AS; Kevin Iott, PlaneWave Instruments; Eric Blackhurst, Planewave Instruments; Justin Fletcher, Space Systems Command A&AS

Keywords: star detection, neural networks, robotic observatories, deep learning, machine learning

Real-Time AI Video Processing for Single-Shot RSO Detection, Classification, and Localization via Ellipse Regression (2025)
Matt Brown, Rocket Lab; William Bidle, Rocket Lab; D. Brandon Knape, Rocket Lab; Brandon Whitchurch, Rocket Lab; Skip Williams, Rocket Lab

Keywords: Machine Learning, Deep Neural Network, SDA, Detection, Classification, RSO, Ellipse

Reinforcement Learning for Space-to-Space Surveillance: Autonomous Scheduling for Resident Space Object Imaging (2025)
Daniel Huterer Prats, University of Colorado Boulder; Hanspeter Schaub, University of Colorado Boulder; Chris Wheeler, Interactive Aptitude

Keywords: Space Situational Awareness, Space Domain Awareness, Reinforcement Learning, Autonomy, Scheduling, Space-to-Space Surveillance

Safe, Autonomous Multi-Agent Inspection of Space Objects Leveraging Relative Orbit Dynamics (2025)
Mark Stephenson, University of Colorado Boulder; Hanspeter Schaub, University of Colorado Boulder

Keywords: inspection, reinforcement learning, multi-agent, autonomy, rpo

Toward Integration of Large Language Models for Command and Control in Space Domain Awareness (2025)
Enrique De Alba, EO Solutions; Alexander Cabello, EO Solutions; Garrett Fitzgerald, EO Solutions; Zach Gazak, Space Systems Command A&AS; Justin Fletcher, Space Systems Command A&AS

Keywords: Large Language Models, Space Domain Awareness, Command and Control, Human-Machine Teaming, Natural Language Processing, Sensor Networks, On-premises AI

Uncertainty-Aware Physics-Informed Machine Learning (PIML) for Cislunar Orbit Determination (2025)
Gregory Badura, Georgia Tech Research Institute; Ebenezer Arunkumar, Georgia Tech Research Institute; Miguel Velez-Reyes, The University of Texas at El Paso; Brian Gunter, Georgia Institute of Technology; Koki Ho, Georgia Institute of Technology

Keywords: Machine Learning, Orbit Determination, Angles-Only, Cislunar, Physics Informed Machine Learning

A Common Task Framework for Testing and Evaluation at the Space Domain Awareness Tools, Applications, and Processing Lab (2024)
Imène Goumiri, Lawrence Livermore National Laboratory; Luc Peterson, Lawrence Livermore National Laboratory; Ashley Cocciadiferro, Lawrence Livermore National Laboratory; Ryan Lee, Lawrence Livermore National Laboratory; Jason Bernstein, Lawrence Livermore National Laboratory

Keywords: Space domain awareness, test and evaluation, machine learning, conjunction, RPO

Action-Free Inverse Reinforcement Learning for Evaluating Satellite Similarity and Anomaly Detection (2024)
David Witman, Slingshot Aerospace; Timothy Olson, Slingshot Aerospace; Brian Williams, Slingshot Aerospace; Dylan Kesler, Slingshot Aerospace; Belinda Marchand, Slingshot Aerospace

Keywords: Machine Learning, Anomaly Detection, Constellations

Backbone Architectures for Space Domain Awareness (2024)
Kyle Merry, Sandia National Laboratories; John Ossorgin, Sandia National Laboratories; Zachary Mekus, Sandia National Laboratories

Keywords: ML/AI, SDA, Transformer, Swin Transformer, Convolutional Neural Network, CNN, Feature Extraction, Backbone

Building Trust in Human-Machine Teaming for Autonomous Space Sensing (2024)
Garrett Fitzgerald, U. S. Space Force; Rachel Morris, Applied Decision Science; Laura G. Militello, Applied Decision Science; Justin Fletcher, USSF/SSC

Keywords: Human-Machine Teaming, Cognitive Task Analysis, Space Domain Awareness, Trust in Autonomy, Telescope C2

Data-Driven Identification of Main Behavioural Classes and Characteristics of Resident Space Objects in LEO Through Unsupervised Learning (2024)
Marta Guimaraes, Neuraspace; Claudia Soares, FCT-UNL; Chiara Manfletti, Neuraspace

Keywords: Space Situational Awareness, Space Debris, Unsupervised Learning, Clustering

Deep Reinforcement Learning Applications to Space Situational Awareness Scenarios (2024)
Benedict Oakes, University of Liverpool; Jason F. Ralph, University of Liverpool; Jordi Barr, Defence Science Technology Laboratory

Keywords: Deep Reinforcement Learning, Machine Learning for SDA Applications, Space Situational Awareness

Detecting Satellites with Object Detection: Challenges of Implementing Deep Learning Techniques for Space-based Images (2024)
Shane Ryall, Defense Research & Development Canada

Keywords: Image Processing, Deep Learning, SSA, NEOSSat

Enhancing Unknown Near-Earth Object Detection with Synthetic Tracking and Convolutional Neural Networks (2024)
Kevin Phan, EO Solutions; Justin Fletcher, Space Systems Command (A&AS)

Keywords: Satellite Detection, Synthetic Tracking, Convolutional Neural Networks, Signal-to-Noise Ratio, Machine Learning, Near-Earth Object Detection, Automation.

Integrating LLMs with SatSim for Enhanced Satellite Tracking and Identification (2024)
Enrique De Alba, EO Solutions; Marco de Lannoy Kobayashi, EO Solutions; Alex Cabello, EO Solutions

Keywords: Large Language Models, Space Domain Awareness, Synthetic Data Generation, Natural Language Processing, Space Surveillance Simulation, Automation in SDA

Machine Learning for E-O Data and Imagery Event Detection (2024)
John Ebeling, Data Fusion & Neural Networks, LLC; Duane DeSieno, Data Fusion & Neural Networks, LLC; Jacob Hansen, Data Fusion & Neural Networks, LLC; Christopher Tschan, Data Fusion & Neural Networks, LLC; Carolyn Sheaff, Air Force Research Laboratory/RIED

Keywords: Electro-Optical (E-O), space surveillance observations, space surveillance imagery, space domain awareness, machine learning

Machine Learning for Space Domain Awareness Sensor Scheduling (2024)
Neil Dhingra, Auria; Cameron DeJac, Auria; Clayton McGuire, Auria

Keywords: Sensor Resource Management, Sensor Tasking, Space Domain Awareness, Space Situational Awareness, Global Optimality, NP Hard Problems, Machine Learning, Supervised Learning, Reinforcement Learning

Neural Network Enhanced Numerical Propagation to Enhance SSA/SDA (2024)
Duane DeSieno, Data Fusion & Neural Networks, LLC

Keywords: Neural networks, Enhanced orbit propagation, Trajectory propagation, Machine Learning

Rapid and Uncertainty Quantified Orbital Propagation Using Uncertainty-Aware AI (2024)
Kevin Vanslette, Raytheon BBN; Alexander Lay, Raytheon BBN; David Kusterer, Virginia Tech Institute for National Security; Kevin Schroeder, Virginia Tech Institute for National Security

Keywords: Artificial Intelligence, Orbital Propagation, Uncertainty

Regularizing Training of Physics Informed Neural Networks (PINNs) for Cislunar Orbit Determination via Transfer Learning (2024)
Gregory Badura, Georgia Tech Research Institute; Miguel Velez-Reyes, University of Texas at El Paso; Brian Gunter, Georgia Institute of Technology; Christopher Valenta, Georgia Tech Research Institute; Koki Ho, Georgia Institute of Technology

Keywords: Machine Learning, Physics-Informed Machine Learning, Cislunar, Space Domain Awareness, Orbit Determination

Resolved Hyperspectral Imaging (2024)
Kimmy Chang, SSC/SZGA; Lauren Fisher, KBR; Zach Gazak, SSC/SZGA; Justin Fletcher, SSC/SZGA

Keywords: Spectroscopy, Machine Learning, Small Telescopes, Hyperspectral, Convolutional Neural Networks, Segmentation, Classification

A Machine Learning Method for Object Localization (2023)
Mridul Gupta, Purdue University; Mary Comer, Purdue University; Edward Delp, Purdue University; Jonathan Chan, Lockheed Martin; Mitchell Krouss, Lockheed Martin; Paul Martens, Lockheed Martin Space; Michael Jacobs, Lockheed Martin; Corbin Spells, Lockheed Martin; Moses Chan, Lockheed Martin;

Keywords: Machine Learning, Object Localization, Closely-Spaced Objects (CSOs), Remote Sensing

AI SSA Challenge Problem: Satellite Pattern-of-Life Characterization Dataset and Benchmark Suite (2023)
Peng Mun Siew, Massachusetts Institute of Technology; Haley Solera, Massachusetts Institute of Technology; Thomas G. Roberts, Massachusetts Institute of Technology; Daniel Jang, Massachusetts Institute of Technology; Victor Rodriguez-Fernandez, Universidad Politécnica de Madrid; Jonathan How, Massachusetts Institute of Technology; Richard Linares, Massachusetts Institute of Technology

Keywords: Behavioral Mode Identification, Pattern-of-Life Characterization, SSA Dataset, Astrometric Data, Machine Learning

End-to-End Behavioral Mode Clustering for Geosynchronous Satellites (2023)
Thomas G. Roberts, Massachusetts Institute of Technology; Victor Rodriguez-Fernandez, Universidad Politécnica de Madrid; Peng Mun Siew, Massachusetts Institute of Technology; Haley Solera, Massachusetts Institute of Technology; Richard Linares, Massachusetts Institute of Technology

Keywords: geosynchronous, station-keeping, pattern of life, maneuver detection, satellite behavior, unsupervised machine learning

Learned Satellite Radiometry Modeling from Linear Pass Observations (2023)
Kimmy Chang, Odyssey Systems--Space Systems Command (A&AS); Justin Fletcher, USSF SSC/SZG

Keywords: Neural Radiance Fields, Machine Learning, Imaging, SSA

Learning Satellite Image Recovery Through Turbulence (2023)
Kimmy Chang, Space Systems Command (A&AS); Justin Fletcher, Space Systems Command (A&AS);

Keywords: Image resolution, telescopes, imaging, atmospheric turbulence, space domain awareness

Light Curve Forecasting and Anomaly Detection Using Scalable, Anisotropic, and Heteroscedastic Gaussian Process Models (2023)
Imene Goumiri, Lawrence Livermore National Laboratory; Amanda Muyskens, Lawrence Livermore National Laboratory; Benjamin Priest, Lawrence Livermore National Laboratory; Robert Armstrong, Lawrence Livermore National Laboratory

Keywords: machine learning, gaussian process, light curve, anomaly detection, space domain awareness, uncertainty quantification

Notable Object Detection from TLE Based on Deep Metric Learning (2023)
Jun Yoshida, NEC Corporation; Ryosuke Nakazawa, NEC; Naoki Yoshinaga, NEC Corporation; Taichiro Sano, NEC; Katsuaki Taya, NEC Corporation; Masatoshi Ebara, NEC Corporation; Ryosuke Togawa, NEC Corporation;

Keywords: Machine Learning, Deep Metric Learning

Optimal background removal using denoising diffusion models (2023)
Ingo Waldmann, Spaceflux; Marco Rocchetto, Spaceflux; Marcel Debczynski, Spaceflux

Keywords: diffusion models, deep learning, generative models, inpainting, background modelling, data fusion

Optimally Convergent Autonomous and Decentralized Tasking with Empirical Validation (2023)
Sam Fedeler, Johns Hopkins University Applied Physics Laboratory; Marcus Holzinger, University of Colorado Boulder; William Whitacre, Draper Laborartory

Keywords: optimal sensor tasking, decentralized decision making, cislunar SSA

Physics-Guided Machine Learning for Satellite Spin Property Estimation from Light Curves (2023)
Gregory Badura, Georgia Tech Research Institute (GTRI); Christopher Valenta, Georgia Tech Research Institute (GTRI)

Keywords: Physics-Guided Machine Learning, Space Domain Awareness (SDA), Machine Learning, Light Curves, Time-Series Classification, Spin State, Space Debris

Physics-Informed Orbit Determination for Cislunar Space Applications (2023)
Andrea Scorsoglio, The University of Arizona; Andrea D'Ambrosio, The University of Arizona; Luca Ghilardi, The University of Arizona; Roberto Furfaro, The University of Arizona; Vishnu Reddy, The University of Arizona

Keywords: Orbit determination, Physics informed neural networks, Cislunar space monitoring

Scalable Multi-Agent Sensor Tasking Using Deep Reinforcement Learning (2023)
Peng Mun Siew, Massachusetts Institute of Technology; Tory Smith, United States Space Force, Massachusetts Institute of Technology; Richard Linares, Massachusetts Institute of Technology; Ravi Ponmalai, Aerospace Corporation

Keywords: Multi-agent Reinforcement Learning (MARL), Graph Neural Network (GNN), Sensor Tasking, Artificial Intelligence (AI), Machine Learning (ML), Deep Reinforcement Learning (DRL)

Seeing Stars: Learned Star Localization for Narrow-Field Astrometry (2023)
Violet Felt, U.S. Space Force; Ian McQuaid, KBR; Peter Thomas, KBR; Sean Sullivan, Pacific Defense Solutions; Jeff Houchard, EO Solutions; Justin Fletcher, USSF SSC/SZG

Keywords: Astrometry, Machine Learning, Convolutional Neural Networks, Transformers, Object Detection, Instance Segmentation, Line Segment Detection

SSA Data Analysis With a Two-Pronged Approach Including Machine Learning for RSO Detection (2023)
Sam Wright, Spaceflux & University College London; Marco Rocchetto, Spaceflux; Ingo Waldmann, Spaceflux

Keywords: Optical Measurements, Deep learning, Astrometry, Data Reduction, SSA

Time-to-Event Data (Survival Analysis) based Modelling of Maneuver Occurrence of Non-Cooperative Satellites (2023)
S Shivshankar, Indian Institute of Science; Debasish Ghose, Indian Institute of Science

Keywords: Space Situational Awareness, Non-Cooperative Satellites, Maneuver Time Estimation, Time-to-Event Data Analysis, Regression Analysis, Machine Learning for SSA Applications

Validity Evaluation of Anomaly Detection Using LSTM Autoencoder for Maneuver Detection (2023)
Ryo Kato, NEC Aerospace Systems, Ltd.; Takahito Sakaue, NEC Aerospace Systems,Ltd.; Daiki Mori, NEC Corporation; Makoto Tanaka, NEC Aerospace Systems, Ltd.; Taichiro Sano, NEC; Katsuaki Taya, NEC Corporation; Masatoshi Ebara, NEC Corporation; Ryosuke Nakazawa, NEC; Jun Yoshida, NEC; Ryosuke Togawa, NEC Corporation

Keywords: Machine Learning, Deep Learning, Metric Learning, Maneuver, SSA/SDA