Pre-conference tutorials have been organized to provide in-depth learning prior to the start of the technical program. Course materials are the intellectual property of the instructor; an electronic copy of notes may be included in the conference 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 12: 8:30 a.m. - 10:00 a.m.
GPS/GNSS 101	Dr. John Raquet
An Introduction to Cryptography	Dr. Joe J. Rushanan and Dr. James T. Gillis
PNT System Design and Deployment Process	Jeremy Gray and Josiah Watson
Introduction to Advanced Satnav Signal and Receiver Design using Python 1	Dr. Sanjeev Gunawardena and Mark Carroll
10:00 a.m. - 10:30 a.m.: Break
Monday, June 12: 10:30 a.m. - 12:00 p.m.
Hands On Exploration of GPS Navigation Concepts	Tim Erbes
Quantum Technologies for PNT	Dr. Brian Kasch
Introduction to Advanced Satnav Signal and Receiver Design using Python 2	Dr. Sanjeev Gunawardena and Mark Carroll
Machine Learning 101 and PNT	Brian Zufelt

GPS/GNSS 101

Time: Monday, June 12, 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 is currently the director of IS4S-Dayton. 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 has published over 170 navigation-related conference and journal papers and taught 60 navigation-related short courses to over 3600 students in many organizations. He is an ION Fellow and past president.

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An Introduction to Cryptography

Time: Monday, June 12, 8:30 a.m. - 10:00 a.m.

This tutorial offers a brief, broad and benign overview of cryptography. The first half of the course will explain the three main cryptographic methods: symmetric ciphers, hashes, and public key cryptography. We will illustrate these methods using a variety of non-navigation examples. We will then segue to the second part of the course, which shows where cryptography is used for navigation.

Dr. Joe J. Rushanan is a principal mathematician in the Communications, SIGINT, & PNT department of The MITRE Corporation. He was part of the M-code signal design team and the L1C signal design team. He was the 2019 recipient of ION’s Capt. P.V.H. Weems award for his sustained contributions to the design of GPS. He currently teaches cryptography for Northeastern University’s Khoury College Cybersecurity graduate program. He received his MS and PhD in mathematics from The Ohio State University and the California Institute of Technology, respectively.

Dr. James T. Gillis, PhD is a senior project Leader at The Aerospace Corporation. He has been involved with GPS since 1983. He was a member of the SAASM development team and co-chair of the GPS Modernization Signal Design Security Team.

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PNT System Design and Deployment Process

Time: Monday, June 12, 8:30 a.m. - 10:00 a.m.

This tutorial outlines the process for developing a PNT system, to both introduce new engineers to this process as well as introduce best practices for development through real examples. Research engineers from The Air Force Institute of Technology's (AFIT) Autonomy and Navigation Technology (ANT) Center will outline and demonstrate the process of developing a PNT system for a research flight test. A problem requiring a PNT system will be presented and associated SysML digital engineering models will be reviewed. From these models, we will discuss how to design a real-world data collection process, an example of using simulated sensor data, and the trade-offs with each approach. From this sensor data, we will walk through an implementation of NavTK in Python and discuss some best practices for developing filter software using Docker with deployment in mind. Finally, we will demonstrate how the development practices used allows for near seamless deployment to the intended system.

Jeremy Gray is the engineering team lead at the Air Force Institute of Technology's (AFIT) Autonomy and Navigation Technology Center (ANT). Jeremy holds a BS in Mechanical Engineering Technology from the University of Dayton and a MS in Systems Engineering from AFIT. For the last seven years at the ANT Center, Jeremy has researched complementary navigation system applications, small unmanned aerial system design and integration, modular open system architectures, digital engineering, cooperative navigation, and autonomous systems.

Josiah Watson has been a staff research engineer with the Autonomy and Navigation Technology (ANT) center at the Air Force Institute of Technology (AFIT) since 2018. He holds a BS in Computer Engineering (2018) from Cedarville University and a MS in Electrical Engineering (2020) from AFIT. He has been involved in projects exploring magnetic navigation and GPS-denied navigation for small unmanned aerial systems (UAS).

Jonathon Accurso is a software engineer with CAL Analytics focusing on aircraft simulation and analysis. He has worked with the ANT Center and AFRL to develop and integrate a variety of components and systems into different simulation software, such as AFSIM and JSBSim, to achieve anything from sensor performance analysis to training AI in flight controls.

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Introduction to Advanced Satnav Signal and Receiver Design using Python 1

Time: Monday, June 12, 8:30 a.m. - 10:00 a.m.

This two-part course aims to provide attendees with a solid understanding of the fundamentals of satellite timing and navigation (satnav) systems. This includes their signal structure, how they’re generated and transmitted at the satellite, received and processed by user equipment, as well as system impairments caused by channel effects and interference. The course is divided into multiple modules – each comprised of a short lecture followed by a software demonstration that reinforces the topics covered.

This course employs a Python-based SDR platform known as PyChips. It introduces a prototype satnav system specification language written in JavaScript Object Notation (JSON). This allows the user to specify space vehicles (SVs) and other emitters with advanced signal structures, generate the received signals at the sample level, and then process and analyze these signals with one or more receivers whose architectures can be specified. In addition to the simulated signals, receivers also support the ION SDR Metadata Standard to process existing sampled data files. An introduction to PyChips can be found in [1].

Part 1: Satnav frequency bands, signal structures, and link budgets; anatomy of a multi-frequency, multi-signal-component SV; modulating multiple signal components onto the same carrier using phase optimized constant envelope transmission (POCET); power spectral density (PSD) analysis of advanced signals; correlation.

Prerequisites: Basic understanding of digital signal processing, object-oriented programming concepts, and the Python programming language are useful but not required for this course. Participation in software demonstrations is optional. Please note that attendees intending to run the demos must ensure adequate laptop battery power since the meeting room will not be equipped with power outlets.

Additional information regarding this course will be posted here. This will include the software environment setup guide and instructions for downloading the software for registered attendees.

[1] Gunawardena, S., ‘A High Performance Easily Configurable Satnav SDR for Advanced Algorithm Development and Rapid Capability Deployment,’ Proceedings of the 2021 International Technical Meeting of The Institute of Navigation, January 2021, pp. 539-554. https://doi.org/10.33012/2021.17808

Dr. Sanjeev Gunawardena is a research associate professor with the Air Force Institute of Technology (AFIT). He leads robust GNSS technology development - one of three major R&D thrusts of the Autonomy and Navigation Technology (ANT) Center at AFIT. His research interests include RF design, digital systems design, high performance computing, SDR, and all aspects of satnav and associated signal processing.

Mark Carroll is an electronics engineer with the Air Force Research Laboratory Sensors Directorate. He received his BS in Computer Engineering and MS in Computational Science and Engineering from Miami University, Oxford Ohio. His research interests include satnav, satnav SDRs, and machine learning.

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10:00 a.m. - 10:30 a.m.: Break

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Hands On Exploration of GPS Navigation Concepts

Time: Monday, June 12, 10:30 a.m. - 12:00 p.m.

In this tutorial the audience will participate in the creation of a room-sized, rudimentary navigation system using simple ropes, rings, and tripods. We will explore GPS navigation concepts such as measurement determination, dilution of precision, clock bias, clock drift, and finally solving the full position velocity and time (PVT) solution. This tutorial is suitable for curious individuals that wish to obtain a physical understanding of these navigation concepts.

Tim Erbes is the technical director at Orolia Defense & Security, an organization specializing in GNSS Testing & Simulation, GPS Jamming & Spoofing Detection, and Resilient PNT Solutions. Erbes’ professional pursuits have focused on simulation, embedded systems, and test automation for GPS in NAVWAR applications. Prior to Orolia, Erbes worked as the chief technology officer for Talen-X, overseeing technical development, quality assurance, and innovation. Earlier on, Erbes played an essential role at Rockwell Collins as a systems engineer for the well-known Military GPS User Equipment (MGUE) program. Erbes received BS degree in Aerospace Engineering from Iowa State University.

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Quantum Technologies for PNT

Time: Monday, June 12, 10:30 a.m. - 12:00 p.m.

This tutorial will introduce quantum technologies for position, navigation, and timing (PNT). The advent of atomic clocks nearly 70 years ago brought about a revolution in PNT for civilian and military applications. It enabled the dissemination of precision timing and accurate positioning via the GPS constellation, which remains the gold standard in navigation. Recent advances in quantum sensing, timing, and enabling technologies promise a new paradigm in PNT. These advancements include the development of inertial sensors and atomic clocks with unprecedented sensitivity and precision, as well as robust lasers for the manipulation of quantum states. Many challenges remain in deploying devices for mobile applications. This tutorial will provide an overview of the field and how these challenges are being met.

Dr. Brian Kasch received his PhD in Tuebingen, Germany, using Bose-Einstein Condensates as near-field detectors of EM noise. His research focuses on ultracold atoms for inertial sensing, deployable miniaturized atomic clocks, and photonic integrated circuits for PNT applications. He joined the Air Force Research Laboratory as a civilian in 2014 and received the 2019 John L. McLucas Basic Research Award for his work in advancing precision sensors based on atomic physics.

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Introduction to Advanced Satnav Signal and Receiver Design using Python 2

Time: Monday, June 12, 10:30 a.m. - 12:00 p.m.

This two-part course aims to provide attendees with a solid understanding of the fundamentals of satellite timing and navigation (satnav) systems. This includes their signal structure, how they’re generated and transmitted at the satellite, received and processed by user equipment, as well as system impairments caused by channel effects and interference. The course is divided into multiple modules – each comprised of a short lecture followed by a software demonstration that reinforces the topics covered.

This course employs a Python-based SDR platform known as PyChips. It introduces a prototype satnav system specification language written in JavaScript Object Notation (JSON). This allows the user to specify space vehicles (SVs) and other emitters with advanced signal structures, generate the received signals at the sample level, and then process and analyze these signals with one or more receivers whose architectures can be specified. In addition to the simulated signals, receivers also support the ION SDR Metadata Standard to process existing sampled data files. An introduction to PyChips can be found in [1].

Part 2: Acquisition engines; signal tracking techniques and control state machines; inter-frequency aiding; measurement computation; Monte Carlo analysis using PyChips.

Prerequisites: Basic understanding of digital signal processing, object-oriented programming concepts, and the Python programming language are useful but not required for this course. Participation in software demonstrations is optional. Please note that attendees intending to run the demos must ensure adequate laptop battery power since the meeting room will not be equipped with power outlets.

Additional information regarding this course will be posted here. This will include the software environment setup guide and instructions for downloading the software for registered attendees.

[1] Gunawardena, S., ‘A High Performance Easily Configurable Satnav SDR for Advanced Algorithm Development and Rapid Capability Deployment,’ Proceedings of the 2021 International Technical Meeting of The Institute of Navigation, January 2021, pp. 539-554. https://doi.org/10.33012/2021.17808

Dr. Sanjeev Gunawardena is a research associate professor with the Air Force Institute of Technology (AFIT). He leads robust GNSS technology development - one of three major R&D thrusts of the Autonomy and Navigation Technology (ANT) Center at AFIT. His research interests include RF design, digital systems design, high performance computing, SDR, and all aspects of satnav and associated signal processing.

Mark Carroll is an electronics engineer with the Air Force Research Laboratory Sensors Directorate. He received his BS in Computer Engineering and MS in Computational Science and Engineering from Miami University, Oxford Ohio. His research interests include satnav, satnav SDRs, and machine learning.

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Machine Learning 101 and PNT

Time: Monday, June 12, 10:30 a.m. - 12:00 p.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. Thru 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 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.

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