SHORT COURSES

Presented by:
Steven Lewis, Principal Engineer/Scientist, JCO

According to Space Doctrine Publication (SPD) 3-0, Operations, “Space Domain Awareness (SDA) is the timely, relevant, and actionable understanding of the operational environment” that includes “situational awareness of operations and threats in the electromagnetic spectrum.” The JCO’s “Protect and Defend” mission includes monitoring and characterizing the electromagnetic operational environment for threats. The JCO conducts this through Electromagnetic Spectrum Operations (EMSO) which includes Positioning, Navigation, and Timing Situational Awareness (PNT-SA) and will be extended to SATCOM and eventually TT&C situational awareness.[1]

This short course will highlight PNT-SA operational products, successes and will summarize future enhancements that are being developed. Space-based situational awareness is a key enabler for awareness and protection of shared spectrum and PNT-SA is providing demonstrable, operational capabilities.

[1] “Joint Commercial Operations (JCO) Introduction and Way Forward,” Ms. Barbara Golf, Ms. Anne Konnath, Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), 2024.

Presented by:
Brandon Jones, Associate Professor, The University of Texas at Austin

This short course will present the fundamentals of Uncertainty Propagation (UP) through the lens of SSA to help attendees understand the implications of how uncertainty may be represented and correctly propagated.  Accurate UP is a fundamental driver for timely and accurate SSA.  Broadly, UP is the science of quantifying, reducing, and understanding sensitivity to uncertainty and how they map through our computer simulations/models and algorithms.  Propagated uncertainty can determine how we interpret knowledge extracted from data and computer simulations, what potential data may help to reduce uncertainty, and ultimately influences operator decisions.  The primary goal of this course is to introduce attendees to the fundamental principles and methods of uncertainty propagation, which they may use in their work and research after this course.

Topics will include definitions of key components of UP, sources of uncertainty in simulations, an overview of existing tools for uncertainty prediction through computer models, and various methods of analyzing sensitivity to sources of uncertainty.  Concepts will be illustrated through SSA-centric applications such as orbit uncertainty propagation and sensor tasking.  Finally, attendees will be introduced to the latest areas of research in UP, including multi-fidelity methods and the intersections of UP and machine learning.

This course is intended for those looking to improve their understanding on how to propagate uncertainty beyond the traditional methods based on linearization.  While anyone working with uncertainty will benefit from this course, it will be especially useful to those using or implementing software tools for simulation and data analysis.  Attendees familiar with basic probability and statistics will have the foundation necessary for this short course.

Presented by:
Roberto Furfaro, Professor and Director of Space4 Center, University of Arizona
Richard Linares, Associate Professor, Massachusetts Institude of Technology
Weston Faber, Senior Scientist, L3Harris

• Understand Foundational DL Architectures: Gain familiarity with core Deep Learning concepts—Dense & Convolutional Neural Networks, Variational Autoencoders—and the critical underpinnings of how they learn from data.
• Explore Transformer-Based Architectures & LLMs: Learn how the Transformer architecture has enabled breakthroughs in natural language processing and sequence modeling tasks, and explore emerging approaches for adapting LLMs to numerical, time-series, and multi-modal data relevant to SSA.
• Deep Reinforcement Learning for SSA: Get introduced to reinforcement learning (Q-learning, Policy Gradient methods) and see how these ideas can be extended using Transformer-based models in advanced control and decision tasks.
• Implement Hands-On Case Studies: Walk through Python-based Jupyter notebooks illustrating how to apply these models (including LLM-driven data processing or analysis pipelines) to TLE data, light curves, and other SDA-relevant problems.
• Discuss Emerging Trends & Challenges: Understand current limitations, ethical considerations, and the future research directions in applying advanced AI—particularly large-scale models—to space domain awareness

Presented by:
Michelle Zhang, PhD Candidate, Princeton University
Ethan Magistro, Master in Public Affairs Student, SINSI Graduate Fellow, Princeton University
Joyce Mo, Technical Staff, Princeton Satellite Systems

This interactive course introduces participants to emerging space technologies through AstroTactics, a fast-paced paper wargame designed to explore the role of advanced systems in modern geopolitics and conflict. Participants will engage with a variety of space technologies, including launch vehicles, on-orbit servicing and active debris removal systems, surveillance, imaging and reconnaissance, and tactical networks, as they consider how these capabilities can support defense operations and situational awareness across land, sea, air, and space domains. Historically, wargames have struggled to integrate space assets as dynamic elements within combat scenarios. This game aims to address that gap by modeling space capabilities as critical, active components of the operational environment.

This 1-hour wargame immerses participants in a fictional scenario involving two countries. The course begins with a briefing on wargaming as a methodology and gameplay directions. Participants then pick from a selection of developing space technologies as their strategic investment for the duration of the game and apply it to shape battlefield outcomes. After playing through the game, the course will conclude with a debrief. Multiple games will be run simultaneously so that participants are able to work in small teams and explore different courses of action.

Participants will engage directly with key space policy and technology considerations, as well as international norms governing space operations. Through gameplay and analysis, they will explore how policy choices—such as policy for support of development in specific technologies—affect strategic outcomes and long-term space resilience. The scenario’s fictional setting allows for creative experimentation while grounded in the real limitations and opportunities presented by today’s evolving space landscape. This holistic approach equips participants with the tools to better understand the future of space and its role in geopolitics.

By the end of the short course, participants will have gained new insights into the strategic value of advanced space systems and a deeper understanding of how wargaming can be used as a tool to assess the impact of technological innovation on future warfare. The wargame is designed to be accessible to players of all backgrounds and does not require prior experience. The wargame can also be adjusted for participant interests.

Presented by:
Aaron J. Rosengren, Assistant Professor, University of California San Diego
Shane D. Ross, Professor, Virginia Tech
Jana Cuberovic, Graduate Student, University of Colorado/Smead Aerospace Engineering Sciences Dept.

The complex motion of distant satellites within the vast Earth-Moon system — shaped by secular, resonant, chaotic, close-encounter, and manifold dynamics — can be examined locally using the perturbed Hamiltonian approach or globally through techniques derived from the restricted three-body problem (R3BP). Beyond the Laplace radius, which defines the dynamical boundary where the xGEO (beyond geosynchronous) multi-body gravitational regime begins, dominant orbital resonances emerge from interactions with the Moon’s orbital and precessional frequencies.

This course will review the foundational astrodynamics in the entire xGEO regime, including lunar mean-motion resonances (MMRs) and secular resonances, as well as the short timescale dynamics of libration-point orbits (LPOs) and and their associated invariant manifolds, and couple this knowledge to the cardinal questions and problems posed by cislunar space domain awareness (SDA). We will discuss the wide variety of dynamical models that are employed to approximate the diversity of trajectories in xGEO space and showcase practical tools for mapping initial conditions and orbits between these models and associated reference frames; including a tool that curates and visualizes xGEO orbits across several reference frames, providing a basis for analysis and understanding of cislunar motion. Whereas circumterrestrial and circumlunar orbits are largely governed by the perturbed two-body problem, in which the effects of the non-spherical gravity field and third-body perturbations on Earth or Moon satellites are often treated in a Hamiltonian formulation, all other cislunar trajectories, including lunar transfers, LPOs, Earth-Moon cyclers, stable and unstable MMRs, and a wealth of other exotic periodic and non-periodic orbits, are specific applications of the gravitational N-body problem.

We will review the multiscale astrodynamics of xGEO space and bridge the gap between the perturbative treatment of distant geocentric orbits and the restricted three-body dynamics of LPOs and MMRs. Combining observations, theory, simulation, and visualization, this course will showcase the phase-space structure and connectivity of xGEO, identifying both highly stable “graveyard” orbits, as well as chaotic-trajectory regimes that can most easily mask maneuvers. We will discuss how this fundamental knowledge can be adapted to processes that support the ground- and space-based surveillance and maintenance of a cislunar catalog, accommodating the complex dynamics in this regime. Our scope covers the wide range of orbital phase space relevant to both historic and current xGEO missions launched by the US (e.g., AMPTE, Chandra X-ray Observatory, several EXPLORER series satellites, ARTEMIS), Europe (e.g., XMM-Newton, Cluster II), Russia (e.g., Prognoz, Spektr-R, Astron), as well as the significant future xGEO missions scheduled or proposed by over a dozen nations or organizations to be launched in this decade. By analyzing a curated catalog of historical and current xGEO ephemerides, we will identify the constraints and boundaries of each theoretical and computational approach, revealing their inherent limitations and the precise domains in which they remain valid. Designed for those with a basic background in Keplerian (two-body) orbital mechanics, this course minimizes mathematical detail in favor of conceptual understanding. Participants will also have the opportunity to contribute to an ongoing research study during the session.

Presented by:
Brian McReynolds, Assistant Professor of Physics, U.S. Air Force
Gregory Cohen, Associate Professor, Western Sydney University
Rachel Oliver, Assistant Professor, Air Force Institute of Technology
Michal Zolnowski, Member of the Board, Remote Observatories for Asteroids and Debris Searching
Zachry Theis, Chemist, AFRL Space Vehicles

Event-based sensors (EBS) are a novel class of optical imaging devices that offer a different way to detect, track, and characterize resident space objects. The technology has already shown promising results for SDA applications, and the pace of both software and hardware improvements is accelerating rapidly.  We plan to build on the well-attended course we offered in 2022 and 2023 to provide a hands-on introduction to this technology and how it can be applied to tackling SDA tasks in a completely different way.  As such, we will update the content to introduce state-of-the-art SDA sensing and processing techniques developed in the past two years, and provide access to some of the newest technology currently available.

EBS have gained popularity in recent years due to the many benefits they offer over conventional, frame-based optical sensors, such as low data rates, low power consumption, high dynamic range, and high temporal resolution. Many of these benefits arise from the technology’s unique operational construct, and make event cameras an attractive technology for Space Domain Awareness (SDA). Rather than sampling each pixel in the array at a set frame-rate like conventional cameras, event cameras have pixels that operate asynchronously and only report binary events that indicate a change in log photocurrent on the activated pixel. Now, after further studies into characterization, observation, and data analysis, this half-day course provides participants with an introduction to the sensors and a hands-on tutorial on how to get started using them for SDA.

The course will start with a few targeted lectures followed by hands-on experience operating an event camera and manipulating data from real-world SDA collections. Lectures will include an overview of EBS, including basic details of the pixel circuitry to help build intuition regarding camera operation, tuning camera settings to optimize detection capability, and interpreting sensor output. The instructors will then cover the basics of operating and calibrating the sensor for SDA operations, with a focus on a specific camera model that will be determined prior to the course and used during the hands-on instruction portion. Participants will be shown examples of good SDA collections. While the instructors will cover lessons learned to obtain high quality recordings, participants will also be invited to discuss their sensing goals to acquire advice that is applicable to their individual needs.  For the hands-on portion, participants will have a chance to manipulate various settings and make recordings with an EBS (make and model TBD) using open-source processing software provided by the instructor team. Participants will be guided through some of the most applicable software features, and have a chance to experience firsthand the effects of tuning the roughly half-dozen bias currents that are most critical to refining sensor performance. Additionally, course participants will be provided access to a repository containing a number of event camera recordings and post processing software. During the course, instructors will provide a guided example of processing a raw dataset, visualizing the data, and running code to extract important information such as star and satellite tracks.

Presented by:
Francis Chun, Professor, USAF Academy
Benjamin Roth, Director of Astronomical Research, USAF Academy
Timothy Giblin, Senior Scientist, i2 Strategic Services LLC
David Strong, Director, Strong EO Imaging, Inc.
Anil Chaudhary, Principal Scientist, Applied Optimization
Phillip Fishbein, Computer Scientist and Mathematician, Applied Optimization

This short course will present how traditional and advanced optical data is collected, accessed, and used by academia, industry, and government for basic research on Space Domain Awareness (SDA). There will be four sessions, which describe methods for data collection, image processing, and analysis of signatures collected using the USAFA Falcon Telescope Network (FTN).

The course will cover sidereal and rate tracking of non-resolved space objects, use of different types of filters, source extraction, and calibration of panchromatic and multi-spectral photometry, spectroscopy, and polarimetry data, and will include analysis of unique features of different types of data. The non-resolved source extraction will include streak processing for sidereal tracking, point spread function (PSF) fitting, and aperture photometry. The image processing techniques will include the extraction of visual magnitude from different types of photometry data, and Stokes parameters from polarimetry. The calibration methods will include the basics of noise sources and their reduction using noise calibration frames. The course will use FTN data, and describe methods to support non-resolved object characterization and identification. The FTN data will include near-simultaneous collection for a deep space object using multiple telescopes. Finally, the course will describe the use of a data standard to facilitate the reporting and exchange of optical data on the Unified Data Library (UDL).

Presented by:
Thomas Schildknecht, Head Optical Astronomie, Astronomisches Institut Universität Bern

The proliferation of space debris and the increased probability of collisions and interference raise concerns about the long-term sustainability of space activities, particularly in the low-Earth orbit and geostationary orbit environments. During recent years governments, space agencies and civilian research organizations increased their efforts to build space object catalogues and to investigate the space debris population in different orbit regions. Understanding the nature and the sources of debris is a prerequisite to provide the scientific foundation for a sustainable use of near-Earth space.

This course will provide a general introduction to the space debris problem, give an overview on the current space debris research activities to detect and characterize space debris, followed by a presentation of the efforts to model the future space debris population and the international efforts to protect and remediate the space environment. Particular focus will be put on optical techniques to detect, track and characterize space objects including small-size debris. The techniques will be illustrated with examples from the long-standing observation programs of the Astronomical Institute of the University of Bern (AIUB).

Presented by:
Moriba Jah, Co-Founder & Chief Scientist, GaiaVerse Ltd.

• Possibility: Assessing the plausibility of observed orbital states and behavior
• Necessity: Determining what must be true by ruling out all viable alternatives
• Credibility: Evaluating how well an actor’s declared intent aligns with observed data
• Surprisal: Quantifying how unexpected a behavior is, and when it warrants escalation or inquiry

• Intent inference and anomaly classification for rendezvous and proximity operations (RPOs)
• Conflict prevention using possibility-based threat models (intent + opportunity + capability)
• Development and use of Notices to Space Operators (NOTSOs) as an orbital analog to aviation’s NOTAMs
• Designing minimal “trust kernels” for shared SSA in Track 2.0 diplomacy contexts

• Reframe traditional SSA challenges through a decision intelligence lens
• Use possibility-based reasoning to support anomaly interpretation and behavioral deconfliction
• Apply structured epistemic primitives to real SSA data fusion and intent modeling problems
• Propose and evaluate cooperative mechanisms for orbital transparency and trust-building

Presented by:
Peter Zimmer, Astronomer, J.T. McGraw and Associates, LLC

This course will provide those new to the space domain awareness (SDA) community (as well as those seeking a refresher – if you already know this stuff, think of how smart you’ll feel!) an introductory-level understanding of the tools and techniques used for detecting and tracking earth-orbiting satellites with ground-based optical instruments. The course begins with an overview of optical telescopes and includes a discussion of many of the key terms and buzzwords one might encounter when reading about ground-based optical telescopes. From there, the course presents an overview of how these components are assembled into a sensor package for nighttime optical SDA and can be optimized to suit various mission goals. This includes a discussion of satellite visual magnitudes, terminator viewing, sensitivity, search rate and related topics. Finally, the course presents a brief look at the challenges and differences of optical systems for daytime optical and cislunar SDA.

Presented by:
Jeff Cornelius, Principal Systems Engineer, Parsons
Kevin Daly, Deputy Director, Advanced Technologies, Parsons

Today’s space environment is saturated with data from satellite telemetry to spacecraft observations to physical force model parameters such as space weather indices and drag profiles. Space Situational Awareness (SSA) engineers and analysts can take advantage of new data analytics techniques to help predict future events or establish patterns in current data sets.

For those new to the concepts of machine learning and predictive analytics, this course will provide an introductory survey of various data analytics techniques. The course will follow a data to insight to decision making construct, beginning with an introduction to machine learning and definitions of terminology within the field. It will continue with a review of the mathematical background required for machine learning and examine the process of storing and cleaning data to achieve an ideal data set to train models.

The course will examine unsupervised and supervised learning methodologies with a particular emphasis on supervised learning techniques. Some of these that will be explored include principal component analysis, K-means clustering, association rule mining, artificial neural networks, random forest ensemble learning, and extreme gradient boosting. Each technique will be evaluated with pros and cons. Additionally, the confusion matrix will be introduced as a method for evaluating the effectiveness of a predictive model. Example code in R will be provided for many of these implementations.

Finally, the course will examine practical applications in the SSA field of these analytics techniques. These will include hypothetical examples as well as current, on-going, real-world projects to predict patterns of life or to understand satellite maneuver intent.

Students in the course should come away with a basic level understanding of data driven analytics and application to SSA. They will understand the common terminology and be able to ascertain if a method or approach is effective and is applicable to specific projects.

Presented by:
Jeffrey Steward, Principal Scientist, Orion Space Solutions
Junk Wilson, Senior Vice President, Orion Space Solutions 
Rachel Stutz, Aerospace Software Engineer, Orion Space Solutions 

• Researchers and practitioners in SSA/SDA.
• Space weather forecasters.
• Satellite operators.
• Students and professionals in related fields (e.g., physics, engineering, computer science).

Presented by:
Mark Muktoyuk, RPO Team Lead | Sr GNC Systems Engineer, Astroscale U.S.

• FD: Flight Dynamics — the planning, monitoring, and commanding of the mission kinematics.
• GNC: Guidance, Navigation, and Control— the execution of the mission kinematics as defined by FD.
• FSW: Flight Software — how the vehicle processes the data received from the ground and from onboard sensors.
• GSW: Ground Software — how the data is relayed from the sensors obtaining SDA data to the mission planners, and in turn relayed to the spacecraft via commands and telemetry.

Presented by:
Shadi Mohagheghi, Senior Application Engineer/Technical Account Manager US Air Force, MathWorks
Reece Teramoto, Senior Application Engineer, MathWorks

Presented by:
Patrick North, Chief Remote Sensing Engineer, Image and Computer Scientist, Ansys Government Initiatives (AGI)

This short course is meant to provide the theoretical background for modeling and simulating Space Situational Awareness (SSA) and Space Domain Awareness (SDA) relevant targets of interest, imaging systems, and environmental conditions in the optical wavelengths from the visible through the longwave thermal infrared.

Imaging both with traditional and novel methodologies is an essential element of SSA/SDA, and this course will provide both a general, detailed, and complete framework for the math, physics, and engineering of the imaging process. This will offer professionals and students directly in SSA/SDA and adjacent fields a deep-dive into the techniques, a guided tour of simulation options available, and most importantly the ability to ask any detailed questions.

In addition to the course a companion field-guide has been developed and will be made available to participants. This is the missing textbook for image simulation including the radiometric equations, constants, and system models along with specifically tailored SSA/SDA specific parameters such as exoatmospheric solar illumination ranges, visual magnitude calculations and variations, thermal emissions values for standard space objects, and more.