JNC Tutorials

Pre-conference tutorials have been organized to provide in-depth learning prior to the start of the technical program. Course materials may be the intellectual property of the instructor. An electronic copy of notes may be made available in the proceedings for qualified attendees at the instructor's discretion.

Tutorials are included with the cost of a full registration. ION reserves the right to cancel a portion of the tutorial program based on availability of the instructor.

Monday, June 3: 8:30 a.m. - 10:00 a.m.
A1: Quantum PNT
Dr. Maxwell Gregoire / Dr. Bonnie Marlow
B1: Signals of Opportunity
Dr. Frank van Graas
C1: Approaches for Resilient & Robust Positioning, Navigation, and Timing (PNT)
Logan Scott
D1: Machine Learning 101 and PNT
Brian Zufelt / Renee Yazdi / Clarizza Morales
E1: GPS/GNSS 101
Dr. John Raquet
F1: Magnetic Navigation (MagNav)
Dr. Aaron Canciani

A1: Quantum PNT

Time: Monday, June 3, 8:30 a.m. - 10:00 a.m.

Amid the Department of Defense’s concerted efforts to create position, navigation, and timing (PNT) that are complementary to GPS, quantum sensors and timing devices are entering the commercial world and being developed for specific military applications and they are rapidly rising in technological readiness. In this context, it is important to understand how the further development of these technologies will lead to improvements in PNT. In this tutorial, we will compare today’s quantum sensors and complementary PNT systems to their classical counterparts, compare different complementary PNT techniques, and provide a broad overview of where quantum sensors are likely, or unlikely, to make an impact on complementary PNT.

Dr. Maxwell Gregoire Dr. Maxwell Gregoire is a physicist at the Air Force Research Laboratory, Space Vehicles Directorate. For the last five years, Maxwell has led an in-house research group and managed acquisition programs developing high-precision inertial sensors using atom interferometry and levitated optomechanics for air and space applications. Maxwell earned his PhD in physics at the University of Arizona where he used atom interferometry to make high-accuracy measurements of alkali atom properties. He earned is BS in Physics and Mathematics at the University of Nebraska, where he conducted theoretical and experimental research studying electron quantum optics and analyzing Large Hadron Collider data.

Dr. Bonnie Marlow Dr. Bonnie Marlow is a physicist specializing in quantum sensors and nonlinear optics. Her work is centered around enabling risk reduction for the development and deployment of quantum technologies, with a focus on atomic sensors. Currently, Bonnie is the leader of the Quantum Sensors Group at MITRE, where she is responsible for technical and strategic leadership in quantum sensing across multiple work programs. Bonnie earned an AB degree in Physics from Bryn Mawr College and AM and PhD degrees in Physics from Duke University. Her doctoral research focused on experimental and theoretical studies of nonlinear optical effects in ultracold atoms. She was also a postdoctoral researcher at the Joint Quantum Institute, where her research focused on non-classical states of light for precision metrology.




B1: Signals of Opportunity

Time: Monday, June 3, 8:30 a.m. - 10:00 a.m.

This course covers space-based radio frequency (RF) signals that were not designed or intended to be used for PNT. Principles of operation with Signals of Opportunity (SoOP) will be presented along with benefits and drawbacks of the incorporation of SoOP into a PNT solution. Position and timing solutions will be reviewed for stationary and dynamic users, including measurement types and quality, dominant error sources, geometry considerations, and expected PNT performance.

Dr. Frank van Graas Dr. Frank van Graas is a Research Professor of Electrical Engineering in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT). He performs research with the Autonomy & Navigation Technology (ANT) Center in the areas of Global Navigation Satellite Systems, signal processing, and integrated navigation systems. Before joining AFIT in 2022, he served on the faculty of Ohio University since 1988. He authored or co-authored over 200 publications and two patents. He is a Fellow, past president and current treasurer of the Institute of Navigation.




C1: Approaches for Resilient & Robust Positioning, Navigation, and Timing (PNT)

Time: Monday, June 3, 8:30 a.m. - 10:00 a.m.

Diverse elements of international infrastructure are critically reliant on GNSS for precise location and time, often in ways that are not obvious. This tutorial provides a high-level perspective on the effects of interference on GNSS receivers and offers some possible threat mitigation approaches and policy recommendations.

The tutorial starts with a discussion of potential GNSS threats and vulnerabilities. Then, after a quick review of how receivers determine position, the focus is on the effects of various interference types on select signals. The effects of ground mobile propagation in limiting effective jammer range are examined. Mitigations such as adaptive arrays, and IMU aiding are discussed.

Civil jamming examples and incidents are covered along with methods to detect, identify, and militate against their effects. In particular, the importance of maintaining situational awareness for establishing environmental context is examined. Techniques for detecting civil spoofing and authenticating signals will be discussed.

Logan Scott Logan Scott has over 40 years of military and civil GPS systems engineering experience. He is a consultant specializing in radio frequency signal processing and waveform design. At Texas Instruments, he pioneered approaches for building high-performance, jamming-resistant digital receivers and adaptive arrays. In 1985, his team built the first all-digital GPS receiver. At Omnipoint (now T-Mobile), he developed spectrum sharing techniques that led to a Pioneer’s preference award from the FCC. Logan has been an active advocate for improved civil GPS location assurance for over 25 years and was the first to describe how civil navigation signals could be authenticated using delayed key concepts central to the Chimera and ACAS signals. For the past seven years he has been developing advanced signal concepts for NTS-3, AFRL, and the University of Colorado. He has also been active in developing LEO system architectures. Logan is a Fellow of the Institute of Navigation and a Senior Member of IEEE. In 2018 he received the GPS World Signals award. He is the author of Interference: Origins, Effects, and Mitigation in PNT21 and in 2022 was awarded the Capt. P.V.H. Weems award for continuing contributions to the art and science of navigation. Logan holds 46 US patents and is a member of the President’s National PNT Advisory Board.




D1: Machine Learning 101 and PNT

Time: Monday, June 3, 8:30 a.m. - 10:00 a.m.

This course introduces the fundamentals of machine learning (ML) and how it applies to position, navigation, and timing (PNT). Basic machine learning concepts like types of ML, importance and collection of data sets, deployment strategies, and the development toolchain will be covered. Some of the common pitfalls in ML developments will be highlighted, along with strategies for avoiding falling victim to the pitfalls. Through example, the ML concepts that are outlined will be employed to demonstrate how ML can be used to speed and facilitate PNT OODA loop closure. The PNT OODA loop will be discussed in the context of the system construct to include architectural elements and concepts of operation. Questions of how to collect, when to collect, what to collect, and where to send the data will be explored, as well as how to respond and automated response options. ML unique requirement considerations and specifications will be outlined, as well as ongoing challenges. Foundational tools developed for addressing the unique challenges of ML applied to PNT will be introduced. Attendees will have the opportunity to experiment with the tools.

This course employs a package of readily available ML tools that are either created or assembled for government use. These tools include a data collection system, a set of reference ML algorithms as a good starting point in that they have a good performance history in a contested environment (affectionately called the model zoo), a ML development and test environment that employs standardized and compatible toolsets, and a set of vetted and conditioned data sets; everything that a ML developer or evaluator needs to get started on applying ML solutions to PNT in a contested environment.

Prerequisites: Basic understanding of optimization and the Python programming language are useful but not required for this course. Those that want to follow-along can bring a laptop, but it is optional.

Brian Zufelt Brian Zufelt serves as the deputy director of Cosmiac from the University of New Mexico's School of Engineering. His current work with the Air Force Research Lab focuses on using machine learning to detect, mitigate, and predict future threats to the GNSS. Also, Mr. Zufelt has experience in machine learning algorithm optimization for various hardware platforms (TPU, GPU, CPU [ARM,x86], FPGA). His interests include optimizing a machine learning solution for specific hardware architecture, critical to achieving a deployed system's lowest possible size, weight, and power requirement.

Renee Yazdi Renee Yazdi currently primarily supports AFRL PNT projects in her role as a System Engineering Consultant for Canyon Consulting. Renee has spent most of her career in and around space. Besides GPS, she has had the privilege to contribute to a variety of system engineering and technology development efforts with emphasis in remote sensing, communications, and missiles.



Clarizza Morales Clarizza Morales started working with COSMIAC in 2019 collaborating with Slingshot Aerospace on the deployment of unmanned aerial vehicle (UAV) applications using computer vision, video, and image processing techniques. She currently works with the Air Force Research Laboratories (AFRL) Space Vehicles Directorate on the development, research, and testing of new hardware/software technologies to advance the GNSS and PNT infrastructure.




E1: GPS/GNSS 101

Time: Monday, June 3, 8:30 a.m. - 10:00 a.m.

This course presents the fundamentals of the GPS, and other GNSS, and is intended for people with a technical background who do not have significant GPS experience. Topics covered include time-of-arrival positioning, overall system design of GPS, signal structure, error characterization, Dilution of Precision (DOP), differential GPS, GPS modernization, and other GNSS systems.

Dr. John Raquet Dr. John Raquet is currently the director of IS4S-Dayton, where he is leading the development of open architecture approaches to developing navigation systems. Previously, he was the founding director of the Autonomy and Navigation Technology (ANT) Center at AFIT. Dr. Raquet has a PhD in Geomatics Engineering from the University of Calgary, an MS in Aero/Astro Engineering from MIT, and a BS in Astronautical Engineering from the USAFA. He is an ION Fellow and past president.




F1: Magnetic Navigation (MagNav)

Time: Monday, June 3, 8:30 a.m. - 10:00 a.m.

This course will focus on practical considerations for implementation of magnetic anomaly navigation systems. The basic theory of how both an Extended Kalman Filter as well as a Particle Filter can use magnetic anomaly maps to navigate will be described. We will show where this theory can break down on real-world implementations. The two practical challenges that must be resolved are platform calibration as well as map errors. We will discuss the current state of the art for magnetic calibration. We will also discuss the real-world factors that influence map error and how to design navigation systems that are robust to these errors. Finally, we will go over a wide tradespace of magnetic anomaly simulations live in class to provide intuition on how magnetic anomaly navigation is influenced by altitude, map variation, velocity, calibration errors, and other factors.

Dr. Aaron Canciani Dr. Aaron Canciani is a senior research scientist with the Leidos Applied Science Division. He focuses on many aspects of GPS-denied navigation systems. His current research focuses are on using AI/ML to reduce drift of inertial systems, geophysical navigation, as well as SAR and Interferometric SAR navigation. Dr. Canciani has been a part of dozens of projects focused on magnetic anomaly navigation and previously led this exciting research area from within the government during a 12-year Air Force career.