Tuesday, September 21: 9:00 a.m. - 12:30 p.m.
Multi-constellation GNSS Signals and Systems ( Video Preview)	Dr. Chris G. Bartone
Indoor Navigation and Positioning ( Video Preview)	Dr. Li-Ta Hsu
Ionospheric Effects, Monitoring, and Mitigation	Dr. Jade Morton
Tuesday, September 21: 1:30 p.m. - 5:00 p.m.
Introduction to Multiband and Multi-Constellation SatNav Receivers using Python	Dr. Sanjeev Gunawardena
Autonomous System Navigation and Machine Learning	Dr. Mike Veth / Dr. Don Venable
GNSS Integrity ( Video Preview)	Dr. Mathieu Joerger

Multi-constellation GNSS Signals and Systems

Live Class: Tuesday, September 21, 9:00 a.m. - 12:30 p.m. CDT
Room: Regency B

Registration fee:
$400 if registered and paid by August 20
$450 if payment is received after August 20

Course Level: Beginner

Tutorials have been designed for an interactive classroom environment and will be live streamed. Courses will not be available for download or future viewing. The live course offers the benefits of an interactive classroom environment.

This course emphasizes the fundamentals of multi-constellation GNSS. The course begins with an overview of GNSS followed by presentation on each of the GNSS 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, datum’s and time
• GNSS antenna & receiver technologies - overview
• GNSS signal structure formats: Carrier, Code, Data
  • Direct Sequence Spread Spectrum; auto and cross correlation
• GPS legacy and modernized signals:
  • GPS SV Blocks
  • Legacy GPS: C/A, P(Y) code and NAV formats
  • Modernized GPS: 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: 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 Ph.D.EE from Ohio University, a M.S.EE from the Naval Postgraduate School and B.S.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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Indoor Navigation and Positioning

Live Class: Tuesday, September 21, 9:00 a.m. - 12:30 p.m. CDT
Room: Regency A

Registration fee:
$400 if registered and paid by August 20
$450 if payment is received after August 20

Course Level: Beginner to Intermediate

This course will provide an overview of the Indoor Positioning and Indoor Navigation (IPIN) system. Starting from the markets and applications using IPIN, we will introduce the popular technologies and sensors related. Then, an IPIN framework will be introduced that consists of the source space, algorithm space, and integration. After introducing the single point positioning (SPP), we will discuss dead reckoning (DR).

Regarding the data sources of SPP, we separate the sources into homogeneous (geometry based) ones and heterogeneous (scene matching/analysis based) ones. The former ones contain the measurements model of RSS-ranging, AOA, TOA and TDOA while the latter ones contain the fingerprint and other transformed data sources that used to match with pre-surveyed databases. The error and limitation of the SPP will be discussed. The popular DR, using inertial, LIDAR, and visual sensors, namely PDR, LO, and VO, is also introduced before the sensor integration. Finally, the integration based on EKF and FGO is briefly introduced.

The course is suitable for the entry-level R&D students, researchers and engineers who will be working on the projects of IPIN. This course will also appeal to the managers and executives who wish to start a new project and application based on IPIN. The course will conclude with a discussion on the future direction of the indoor positioning system with the coming IoT and 5G era.

Dr. Li-Ta Hsu, born in Taiwan, is an assistant professor in Hong Kong Polytechnic University where he directs the Intelligent Positioning and Navigation Lab focused on the navigation for pedestrian and autonomous driving in urban canyons. His research interest is positioning in GNSS challenged environments.

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Ionospheric Effects, Monitoring, and Mitigation

Live Class: Tuesday, September 21, 9:00 a.m. - 12:30 p.m. CDT
Room: Regency C

Registration fee:
$400 if registered and paid by August 20
$450 if payment is received after August 20

Course Level: Beginner to Intermediate

Ionospheric effects are major threats to the availability, continuity, and accuracy of GNSS solutions. Models, global networks of GNSS stations, and LEO satellite-based radio occultation constellations have been established to monitor and predict the ionospheric effects.

This course will present an overview of the current state-of-art understanding of the various ionospheric effects on GNSS-based navigation systems and their mitigation techniques. The course consists of five parts. The first part is a review of the fundamental properties of the ionosphere that impact satellite navigation signals and PVT solutions. The second part discusses the ionospheric refractive effects, their contributions to the GNSS measurement model, Total Electron Content (TEC) estimation techniques and TEC products, higher order refraction errors, and refractive effect correction techniques. Part three covers the ionospheric scintillation effect, including a brief overview of radio wave propagation through the plasma irregularities, followed by climatology and morphology of scintillation occurrences, and the impact of scintillation on RTK and PPP systems. Part four takes a deeper look into GNSS receiver carrier tracking algorithms designed to combat ionospheric scintillation for ground- and LEO satellite-based receivers. Part five will provide an update on the latest development in space weather monitoring and forecasting using machine learning algorithms and worldwide GNSS observations. We will finish the course with an outlook for outstanding challenges in the field.

Dr. Jade Morton is an Aerospace Engineering professor at the University of Colorado, Boulder. Her research interests lie at the intersection of satellite navigation technologies and remote sensing of Earth’s ionosphere, atmosphere, and surface. She is a recipient of ION Thurlow, Burka, Kepler, and IEEE Kershner award and a Fellow of IEEE, ION, and RIN.

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Introduction to Multiband and Multi-Constellation SatNav Receivers using Python

Live Class: Tuesday, September 21, 1:30 p.m. - 5:00 p.m. CDT
Room: Regency C

Registration fee:
$400 if registered and paid by August 20
$450 if payment is received after August 20

Course Level: Beginner to Intermediate

This hands-on course aims to provide attendees with a solid understanding of the fundamentals of satellite timing and navigation (satnav) software receivers and associated signal processing. The course is divided into multiple modules, each comprised of a short lecture followed by the completion of a Python project that reinforces the concepts and techniques covered. By the end of the 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 open-source code 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 engines
• Correlation across satellite-referenced time epochs on data referenced to receiver epochs: the split-sum correlator
• 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 and BeiDou
• Tracking channel control state machines
• Measurement computation (pseudorange, accumulated Doppler range/carrierphase)
• Effects of fixed-point processing on tracking performance and measurement accuracy
• (New!) Low-power dual-frequency (L1/E1 and L5/E5a) signal processing techniques similar to those used in modern smartphones

Pre-requisites and equipment: Basic understanding of digital signal processing, object-oriented programming concepts and the Python programming language are required to work on the partially complete software projects provided. Attendees must supply their own laptop computers with adequate battery power. The instructor will provide relevant information and software to registered attendees in advance of the course.

Dr. Sanjeev Gunawardena is a research assistant professor with the Autonomy & Navigation Technology (ANT) Center at the Air Force Institute of Technology (AFIT). He has over 20 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.

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Autonomous System Navigation and Machine Learning

Live Class: Tuesday, September 21, 1:30 p.m. - 5:00 p.m. CDT
Room: Regency A

Registration fee:
$400 if registered and paid by August 20
$450 if payment is received after August 20

Course Level: Beginner to Intermediate

The revolution in autonomous vehicle development is providing novel solutions in an ever-growing range of applications. A critical component of autonomous vehicle design is the navigation system, which is required to provide a robust, accurate, navigation solution in a wide range of operating environments. In this short course, we explore the concepts and technology associated with developing and testing navigation systems for autonomous vehicles by combining nonlinear multi-sensor fusion techniques and artificial neural networks (i.e., deep learning).

The course begins with an overview of sensors commonly used for autonomous systems including inertial sensors, GNSS, laser scanners, and image-based sensors. The associated error models are developed for each sensor and examples are presented regarding performance using experimental data. Next, core nonlinear filtering techniques are developed, which support integration with the output of deep learning algorithms.

Finally, an overview of deep learning approaches for autonomous system navigation and associated performance capabilities is presented. The tutorial will begin with an overview of artificial neural network frameworks including: convolutional neural networks (CNN) and recurrent neural networks (RNN). The development will include both a theoretical and algorithmic perspective along with a review of hardware requirements for real-time implementation. Emphasis will be placed on designing a deep learning-based approach for ground vehicle navigation using a monocular camera sensor and open-source data.

Dr. Mike Veth is the co-founder of Veth Research Associates. His research focus is applying nonlinear estimation theory to optimally combine a wide range of sensors and non-traditional navigation sources to enable robust autonomous applications. He received his BS in Electrical Engineering from Purdue University and a PhD in Electrical Engineering from the Air Force Institute of Technology. He has served the ION as Eastern Region Vice President, Dayton Section chair, session chair, track chair and program chair. Dr. Veth has authored over 40 technical articles and book chapters in areas relating to computer vision, navigation and control theory. He is a member of the ION, a Senior Member of the IEEE and a graduate of the US Air Force Test Pilot School.

Dr. Don Venable is currently a principal researcher at Veth Research Associates. Previously, he was a senior electronics engineer at the Navigation and Communications Branch of the Air Force Research Laboratory (AFRL), Sensors Directorate. His research focus is combining probabilistic deep learning with traditional Bayesian estimation theory for non-GPS navigation and object tracking applications. He received his PhD from the Air Force Institute of Technology and both his MS and BS in Electrical Engineering from Ohio University. For his dissertation research, he designed and built a novel optical navigation system for airborne applications. Dr. Venable is active in the Institute of Navigation.

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GNSS Integrity

Live Class: Tuesday, September 21, 1:30 p.m. - 5:00 p.m. CDT
Room: Regency B

Registration fee:
$400 if registered and paid by August 20
$450 if payment is received after August 20

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.

In Part 1, we will define navigation safety metrics and requirement parameters including integrity and continuity risks, alert limit, time to alert, and exposure period. We will review the three major over-bounding methods used to derive high-integrity signal-in-space error models. We will show the impact a GNSS fault such as, for example, an excessive satellite clock drift. We will outline how fault-detection and integrity-monitoring responsibilities can be allocated between reference and user receivers and how prior probabilities of satellite faults can be evaluated and accounted for in the integrity risk equation. We will highlight current limitations in these approaches.

In Part 2, we will 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 Receiver Autonomous Integrity Monitoring (RAIM) and Advanced RAIM; we will use graphical tools of failure mode curves and parity space representations to explain the differences between solution separation and chi-squared residual-based approaches. We will also show recent developments in ARAIM intended to optimize ARAIM integrity and continuity monitoring performance while limiting computational load.

In Part 3, we will review current efforts, including 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 network real time kinematic (NRTK) 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) and Early Achievement Award (2014). He is the senior editor on Navigation for IEEE TAES and a member of EU/US ARAIM Working-Group-C and of RTCM’s Integrity Monitoring for High Precision Applications (SC-134). He received his PhD from Illinois Institute of Technology.

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