Tuesday, September 22: 9:00 a.m. - 12:30 p.m.
Multi-constellation GNSS Signals and Systems	Dr. Chris G. Bartone
Introduction to Multiband and Multi-Constellation SatNav Receivers using Python	Dr. Sanjeev Gunawardena
Tuesday, September 22: 1:30 p.m. - 5:00 p.m.
Indoor Navigation and Positioning ( Video Preview)	Dr. Li-Ta Hsu
Autonomous System Navigation and Machine Learning	Dr. Mike Veth / Dr. Don Venable

Multi-constellation GNSS Signals and Systems

Time: Tuesday, September 22, 9:00 a.m. - 12:30 p.m.

Registration fee:
$400 if registered and paid by August 21
$450 if payment is received after August 21

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 PhD EE from Ohio University, a MSEE from the Naval Postgraduate School and BSEE 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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Introduction to Multiband and Multi-Constellation SatNav Receivers using Python

Time: Tuesday, September 22, 9:00 a.m. - 12:30 p.m.

Registration fee:
$400 if registered and paid by August 21
$450 if payment is received after August 21

Course Level: Beginner to Intermediate

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 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 SDR based 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, including the new GPS L1C signal
• Inter-frequency aiding
• Tracking channel state machines
• Measurement computation
• Effects of fixed-point processing on tracking performance and measurement accuracy
• Performance acceleration using multi-threading and vectorization

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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Indoor Navigation and Positioning

Time: Tuesday, September 22, 1:30 p.m. - 5:00 p.m.

Registration fee:
$400 if registered and paid by August 21
$450 if payment is received after August 21

Course Level: Beginner to Intermediate

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 will provide an overview of Indoor Positioning and Indoor Navigation (IPIN) methods including, geometry-based positioning, scenario analysis (such as fingerprinting), proximity detection and dead reckoning.

Starting from the markets and applications using IPIN, we will introduce the popular technologies and related sensors including: radio signals, inertial measurements and mechanical waves etc., used in indoor positioning methods. 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, the course will separate the sources into homogeneous sources (geometry based) and heterogeneous sources (scene matching/analysis based). 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 are used to match with pre-surveyed databases. Afterwards, the algorithms for SPP including, LS, NLS, ML, MAP and MMSE are introduced. The error and limitation of the SPP will be discussed. The popular DR using inertial and visual sensors, namely PDR and VO, will also be discussed before the sensor integration. Integration based on HMM, EKF and PF will be briefly introduced.

The course will conclude with a discussion on the future direction of indoor positioning system with the coming IoT and 5G era.

This course is suitable for entry-level R&D students, researchers and engineers; and managers and executives desirous to start a new project/application based on IPIN.

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. He is currently a Technical Representative serving on ION Council and an Associate Fellow in the RIN.

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Autonomous System Navigation and Machine Learning

Time: Tuesday, September 22, 1:30 p.m. - 5:00 p.m.

Registration fee:
$400 if registered and paid by August 21
$450 if payment is received after August 21

Course Level: Beginner to Intermediate

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.

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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