Tuesday, September 9: 8:30 a.m. - 12:00 p.m.
Introduction to Multi-Constellation GNSS Signals	Dr. Chris G. Bartone, P.E.
Integrated Navigation Systems for Aviation	Dr. Andrew Neish / Andrew Videmsek
Kalman Filtering	Dr. Penina Axelrad
The Generation and Application of Precise Time	Dr. Michael J. Coleman
Tuesday, September 9: 1:30 p.m. - 5:00 p.m.
GNSS Integrity	Dr. Mathieu Joerger
Space-Based Lunar PNT	Dr. Grace Gao
Factor Graphs for Navigation	Dr. Clark Taylor / Dr. Ryan Watson
Introduction to Software Defined GNSS Receivers and Signal Processing	Dr. Sanjeev Gunawardena

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Introduction to Multi-Constellation GNSS Signals

Date/Time: Tuesday, September 9, 8:30 a.m. - 12:00 p.m.
Recording: Course registrants who do not attend the live course in-person may view a recording of the course one time within 30 days.

Registration fee:
$450 if registered and paid by August 8
$500 if payment is received after August 8

Course Level: Beginner

This course emphasizes the fundamentals of multi-constellation GNSS.  The course begins with an overview of GNSS followed by presentations on each of the GNSSs in operation and/or development today. The course will highlight common features of the various GNSSs and point out key differences between them. Topics to be covered include:

• GNSS segments; space, ground, user segments
• GNSS link budget
• Fundamental concept of GNSS position and time determination
• GNSS coordinate frames and datums and time
• GNSS signal structure formats: carrier, code, data
  • Direct sequence spread spectrum
• GNSS antenna & receiver technologies – overview
• GPS Legacy: C/A, P(Y) code and NAV formats
• GPS Modernized: L2C, L5, L1C, CNAV and CNAV-2 formats
• GLONASS
  • GLONASS SV versions
  • Legacy C/A, P codes and FDMA signals
  • Modernized CDMA codes and frequencies
• Galileo, E1, E6/E6P, E5a, E5b, AltBOC, SAR Codes, frequencies and data formats
• BeiDou, BDS I, BDS II, BDS III, B1, B2, B3 signals and formats
• SBAS used throughout the globe
• QZSS, L1, L2, L5, L6 signals, codes, and services
• NAViC: L1, L5, S band signals, message types
• GNSS corrections for clock, code, atmospheric, transit time, etc.  
• GNSS user solutions

Dr. Chris G. Bartone, P.E., is a professor at Ohio University with over 35 years of professional experience and is an ION Fellow.  He received his PhD EE from Ohio University, a MS EE from the Naval Postgraduate School, and BS EE from Penn State.  Dr. Bartone has developed and teaches a number of GPS, radar, wave propagation and antenna classes. His research concentrates on all aspects of navigation.

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Integrated Navigation Systems for Aviation

Date/Time: Tuesday, September 9, 8:30 a.m. - 12:00 p.m.
Recording: Course registrants who do not attend the live course in-person may view a recording of the course one time within 30 days.

Registration fee:
$450 if registered and paid by August 8
$500 if payment is received after August 8

Course Level: Beginner to Intermediate

Navigation systems have progressed throughout the years to enable aircraft to safely operate in nearly any environment. This tutorial will cover the importance of navigation as it pertains to aircraft operations across all phases of flight; from takeoff and departure to en route operations, approach, and landing. Concepts of integrity, accuracy, availability, and continuity will be covered and how these requirements vary across different phases of flight.

The tutorial will cover navigation infrastructure that supports aviation such as Global Navigation Satellite Systems (GNSS), Satellite-Based Augmentation Systems (SBAS), Ground-Based Augmentation Systems (GBAS) as well as traditional ground-based navigation aids such as Very High Frequency (VHF) Omnidirectional Range (VOR), Distance Measuring Equipment (DME), Non-Directional Beacons (NDB), and Instrument Landing Systems (ILS). The tutorial will also cover onboard navigation sensors such magnetic compasses, Inertial Navigation Systems (INS), Air Data Computers (ADC), and Radar Altimeters. The advent and adoption of these systems will be covered giving context to their origin and their purpose.

In addition to the navigation systems and sensors themselves, an overview of integration techniques such as Kalman Filters will also be covered providing an example of how these systems are combined to produce an integrated navigation solution.

This tutorial will also cover Performance-Based Navigation (PBN) such as Required Navigation Performance (RNP) and Localizer Performance with Vertical Guidance (LPV) and their associated navigation requirements.

We’ll wrap up with a look to the future for systems that are on the horizon and what is necessary to support emerging technologies in international airspace.

Dr. Andrew Neish is a Senior Navigation Engineer at Reliable Robotics where he works to advance navigation capabilities for unmanned aircraft. He received his undergraduate degrees from UC Davis, and his MS and PhD degrees from Stanford University in the GPS Lab under Per Enge and Todd Walter.

Andrew Videmsek is a Navigation Engineer at Reliable Robotics focused on integrating uncrewed aircraft into the National Airspace System. Andrew received his MS in Electrical Engineering from Ohio University in 2020. In 2021 he received the RTCA Jackson award for his research on GPS augmentation for uncrewed aircraft autoland.

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Kalman Filtering

Date/Time: Tuesday, September 9, 8:30 a.m. - 12:00 p.m.
Recording: Course registrants who do not attend the live course in-person may view a recording of the course one time within 30 days.

Registration fee:
$450 if registered and paid by August 8
$500 if payment is received after August 8

Course Level: Beginner to Intermediate

The Kalman filter is a powerful recursive estimation algorithm and it lies at the intersection of probability theory and linear algebra. Its effectiveness stems from its ability to optimally estimate the state of a dynamic system in the presence of uncertain, noisy measurements. Probability theory provides the framework to model this uncertainty, as the filter relies on probabilistic models for both the system dynamics and measurement processes. Linear algebra serves as the mathematical backbone for implementing the filter's computational efficiency. Understanding both probability and linear algebra is crucial for comprehending underlying principles and effectively applying the filter in various real-world scenarios.

Concepts to be covered include matrix operations, state-space representation of dynamical systems, recursive linear least squares, and pure prediction. Probability concepts will be explored to grasp the stochastic nature of measurement and process noise, integral in the Kalman filter framework. Key topics in probability include conditional probability, probability distributions such as Gaussian distributions, expected values, and covariance matrices, crucial for modeling uncertainty in navigation systems. Finally, we will conclude by discussing strategies for tuning and evaluating the performance of different filters, ensuring optimal functionality in real-world applications.

Dr. Penina Axelrad is Joseph T. Negler Professor of Aerospace Engineering Sciences at the University of Colorado Boulder. Her research interests include technology and algorithms for position, navigation, timing, and remove sensing – especially in spaceborne applications. She is a past ION president, a Fellow of ION and AIAA, and a member of the National Academy of Engineering.

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The Generation and Application of Precise Time

Date/Time: Tuesday, September 9, 8:30 a.m. - 12:00 p.m.
Recording: Course registrants who do not attend the live course in-person may view a recording of the course one time within 30 days.

Registration fee:
$450 if registered and paid by August 8
$500 if payment is received after August 8

Course Level: Beginner to Intermediate

Time and frequency are fundamental concepts in the design of many technologies, especially the Global Navigation Satellite Systems (GNSS) on which we are quite reliant for navigation. Even alternative sources of navigation and legacy approaches to geo-positioning rely upon precise clocks. Industries from utilities to financial markets are also dependent on time synchronization with ever increasing needs for precision. For these reasons, understanding the base measurements and realizations of time can be useful for a growing number of research and development areas. This tutorial aims to introduce the foundational elements of time, from its definition to its dissemination into the user segment.

The main topics this course will cover are: the definition and realization of the SI second; generic design of various atomic frequency standards; statistics and metrics quantifying clock performance; the generation of international timescales and local clock ensembles; the comparison of remote timescales or clocks; and applications of these elements to various industries as well as navigation accuracy. Since foundations of precise time are the main thrust, there will be heavier emphasis on the former topics listed above. Important time comparison techniques including two way time transfer will be covered. The material will also highlight recent findings from the timing community on important topics regarding time and frequency and their dissemination by GNSS.

This course is geared towards scientists without significant experience in time metrology or clocks who desire an introduction to these topics. It will be assumed that attendees have a high-level understanding of the role of GNSS clocks, but nothing about the specifics topics mentioned above.  

Dr. Michael J. Coleman is a research mathematician in the Naval Center for Space Technology at the US Naval Research Laboratory in Washington, DC. He is also head of the Systems Analysis Section within the Space PNT Branch. His main work at NRL has been development of the next generation GPS timescale and alternative or experimental precise time dissemination systems. Dr. Coleman chairs the Clock Products Committee of the International GNSS Service and is a delegate to the BIPM’s Consultative Committee on Time and Frequency.

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GNSS Integrity

Date/Time: Tuesday, September 9, 1:30 p.m. - 5:00 p.m.
Recording: Course registrants who do not attend the live course in-person may view a recording of the course one time within 30 days.

Registration fee:
$450 if registered and paid by August 8
$500 if payment is received after August 8

Course Level: Intermediate

This course will describe (Part 1) fundamental concepts in GNSS integrity, (Part 2) successful implementations in aviation applications, and (Part 3) major challenges in future autonomous navigation for air, ground, and sea transportation. The course will emphasize Receiver Autonomous Integrity Monitoring (RAIM); it will include a handout on RAIM theory and a set of problems with solutions and MATLAB codes. In 2025, Part 3 will include an overview of Kalman filter (KF) integrity monitoring.

In Part 1, we will define navigation safety metrics and requirements including integrity and continuity risks, alert limit, and time to alert. We will identify the three major over-bounding methods used to derive high-integrity signal-in-space error models. We will define GNSS faults including, for example, excessive satellite clock drifts. We will outline how integrity-monitoring responsibilities can be allocated between reference-station and user receivers and how prior probabilities of satellite faults are evaluated.

In Part 2, we will briefly describe the major implementations used in aviation applications: the Ground-Based Augmentation Systems (GBAS), the Space-Based Augmentation Systems (SBAS) and the Aircraft-Based Augmentation System (ABAS). We will focus on RAIM and Advanced RAIM; we will use graphical tools of failure mode curves and parity space representations to identify differences between solution separation and chi-squared approaches. We will show recent developments in ARAIM intended to optimize ARAIM integrity and continuity monitoring performance while limiting computational load.

In Part 3, we will review recent efforts in standard developments and performance evaluations to achieve safe navigation in aviation, maritime, railway, and automotive applications. We will discuss recent research on robust modeling of measurement error time correlation that enables high-integrity Kalman filtering of combined GNSS and inertial data. We will identify major challenges in implementing precise point positioning (PPP) and real time kinematic (RTK) to simultaneously achieve high accuracy and high integrity.

Dr. Mathieu Joerger is an assistant professor at Virginia Tech, recipient of ION’s Parkinson Award (2009), Early Achievement Award (2014), Burka Award and Thurlow Award (2023). He is the senior editor on Navigation for IEEE TAES and a member of the EU/US ARAIM Working-Group-C for the FAA. He received his PhD from Illinois Institute of Technology.

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Space-Based Lunar PNT

Date/Time: Tuesday, September 9, 1:30 p.m. - 5:00 p.m.
Recording: Course registrants who do not attend the live course in-person may view a recording of the course one time within 30 days.

Registration fee:
$450 if registered and paid by August 8
$500 if payment is received after August 8

Course Level: Beginner

We are entering a new era of Moon exploration. There are more than forty missions planned within the next decade by ten space agencies, not even counting the efforts of private sector companies like SpaceX and Blue Origin. After more than fifty years since the Apollo program, NASA's Artemis mission will land humans on the Moon including the first woman and first person of color. Exploring the Moon also serves as a crucial stepping-stone for the success of future deep space missions. With the increase in human and robotic exploration, we must provide position, navigation, and timing (PNT) services anywhere on the Moon. In this tutorial, we will cover the following topics.

• An introduction to currently planned missions for lunar exploration
• Main lunar navigation services and Key Performance Indicators (KPIs)
• Overview of candidate orbits for lunar navigation
• End-to-end navigation architecture
• Candidate orbit determination and time synchronization (ODTS) baseline solution for lunar navigation
• Lunar reference frames
• Frequencies selected for lunar navigational signals
• Lunar PNT engine for different user missions, such as descent and landing and rover surface PNT

Dr. Grace Gao is faculty in the Department of Aeronautics and Astronautics at Stanford University. She leads the Navigation and Autonomous Vehicles Laboratory (NAV Lab). Her research is on robust and secure position, navigation and time (PNT) with applications to manned and unmanned aerial vehicles, autonomous driving cars, as well as space robotics.

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Factor Graphs for Navigation

Date/Time: Tuesday, September 9, 1:30 p.m. - 5:00 p.m.
Recording: Course registrants who do not attend the live course in-person may view a recording of the course one time within 30 days.

Registration fee:
$450 if registered and paid by August 8
$500 if payment is received after August 8

Course Level: Intermediate (attendees should have an introductory knowledge of filtering)

While the Kalman Filter (KF) family (linear KF, EKF, UKF, etc.) has been the workhorse of navigation systems for several decades, the factor graph is a generalization of the Kalman Filter that offers improved performance for non-linear systems and is more easily applied to complex systems.  The goal of this tutorial is to take a practitioner who is familiar with the Extended Kalman filter and introduce them to factor graphs.  By the end of the tutorial, the attendants should be able to create a simple factor graph system and will have been exposed to some of the more advanced concepts that make factor graphs an exceptional choice for navigation problems.

More specifically, this tutorial will introduce the factor graph representation of dynamic systems and how this representation is equivalent to a weighted least squares problem that can be solved with sparse matrix computational tools.  We will demonstrate the (surprisingly low) computational costs of factor graphs and methods used to keep those costs low.  We will also introduce popular software packages that can be used to solve factor graph problems, including GTSAM.  Complex estimation problems that can be difficult to handle with other estimation frameworks will be introduced in the factor graph framework and example solutions to these problems will be demonstrated.

Dr. Clark Taylor is the Autonomy and Navigation Technology (ANT) center director and an associate professor at the Air Force Institute of Technology. He received his PhD from University of California, San Diego in 2004 and previously worked as a senior research engineer with the Air Force Research Laboratory and an assistant professor in electrical engineering at Brigham Young University.

Dr. Ryan Watson works at Albedo, developing an orbit determination and mission design framework for their VLEO constellation. He previously worked at the NASA Jet Propulsion Laboratory and the Johns Hopkins University Applied Physics Laboratory on problems related to state estimation/data fusion for robotic and space missions. He holds a PhD from West Virginia University.

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Introduction to Software Defined GNSS Receivers and Signal Processing

Date/Time: Tuesday, September 9, 1:30 p.m. - 5:00 p.m.
Recording: Course registrants who do not attend the live course in-person may view a recording of the course one time within 30 days.

Registration fee:
$450 if registered and paid by August 8
$500 if payment is received after August 8

This course aims to provide attendees with a solid understanding of the fundamentals of satellite timing and navigation (satnav) software receivers and associated signal processing, with a focus towards implementation. The course is divided into multiple modules, each comprised of a short lecture followed by a software demo that reinforces the concepts and techniques covered. By the end of this course, attendees will have an easy-to-use satnav software receiver running on their laptop that takes multiband live-sky sampled data files, acquires and tracks visible open satnav signals, and outputs signal observables. This receiver is fully configured using JavaScript Object Notation (JSON) files such that modification of the source code is not required. It may be further extended to support numerous advanced research applications.

Topics covered:

• Overview of satnav bands, signal structures, link budget, and receiver architecture
• FFT-based signal acquisition and adapting circular correlation to long spreading codes
• Software-based methods for correlation acceleration: bit-wise parallelism, multi-threading, SIMD
• Carrier tracking loops: FLL, PLL and FLL-aided-PLL
• Code tracking loops: DLL, non-coherent vs. coherent tracking, correlator spacing, and carrier aiding
• Tracking of open satnav signals: GPS, GLONASS, Galileo, BeiDou, NavIC, and SBAS
• Internal decision making and control procedures based on signal environment and application
• Measurement computation (pseudorange, accumulated doppler range/carrierphase)
• Direct instantiation for multi-frequency tracking (e.g. Galileo E1 to E5a/b)
• Inter-frequency aiding and duty cycling techniques for low-power applications (e.g. L1/L5 smartphones)

Pre-requisites and equipment: Basic understanding of digital signal processing, object-oriented programming concepts and the Python programming language are helpful but not required to attend this course. Numerous fully-functional demo projects will be provided. If intending to run the demos during the course, attendees must supply their own laptop computers with adequate battery power. The instructor will provide relevant information to registered attendees in advance of the course.

Dr. Sanjeev Gunawardena is a research associate professor with the Autonomy & Navigation Technology (ANT) Center at the Air Force Institute of Technology (AFIT). He has 25+ years of experience in RF, digital and FPGA-based system design. His expertise includes satnav receiver design, advanced satnav signal processing and implementation. Dr. Gunawardena received a BS in engineering physics and a BSEE, MSEE and PhD EE from Ohio University. He is an ION Fellow and member of the ION Executive Committee.

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