Tuesday, September 22: 9:00 a.m. - 12:00 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
Kalman Filter Applications to Integrated Navigation 1	Dr. James L. Farrell / Dr. Frank van Graas
Tuesday, September 22: 1:30 p.m. - 5:00 p.m.
Indoor Navigation and Positioning	Dr. Li-Ta Hsu
Autonomous System Navigation and Machine Learning	Dr. Mike Veth / Dr. Don Venable
Kalman Filter Applications to Integrated Navigation 2	Dr. James L. Farrell / Dr. Frank van Graas

Multi-constellation GNSS Signals and Systems

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

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

Course Level: Beginner

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. - 12:00 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

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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Kalman Filter Applications to Integrated Navigation 1

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

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

Course Level: The course is at the beginner-level and will enhance understanding of the principles of filtering at the beginner and intermediate levels.

The focus of this course is on the basic theory, an intuitive understanding as well as practical considerations, for the design and implementation of Kalman filters. Although many new types of filters are published in the literature, the Kalman filter is still the optimal and most efficient solution for the majority of integrated navigation systems. The course starts with a review of statistics and detailed insights into the most important noise processes, including random walk and Gauss-Markov processes. This is followed by a review of state variables and an overview of Kalman filters, including linear, linearized and extended filters. Matlab®-based examples are provided to facilitate hands-on experience with Kalman filters for integrated navigation applications.

For those having no previous experience with modern estimation, a review of fundamentals is included. Linear systems are characterized in terms of (1) a vector containing the minimum number of independent quantities required to define its state at any instant of time and (2) a matrix expression capable of propagating that state from one time to another. In combination with expressions relating measurements to states, a standard cycle is formed whereby a system's entire time history is continuously produced, with the best accuracies achievable from any combination of sensors, extravagant or austere, providing any sequence of measurements that can be incomplete, intermittent and indirect, as well as imprecise. That already wide versatility is broadened further by straightforward extension to systems with nonlinearities (Extended Kalman Filter; EKF) which has proved adequate for a host of applications (including some to be discussed in this tutorial). The relation between Kalman (sequential) and block (weighted least squares) estimation is illustrated, and a number of important subtleties that often go unrecognized will be uncovered.

Dr. James L. Farrell is an ION Fellow and author of over 80 journal and conference manuscripts. He authored Integrated Aircraft Navigation (Academic Press, 1976) and GNSS Aided Navigation and Tracking (2007). His technical experience includes teaching appointments at Marquette and UCLA, Honeywell, Bendix-Pacific, and Westinghouse in design, simulation, and validation/ test for modern estimation algorithms in navigation and tracking applications, and digital communications system design. As president and technical director of VIGIL INC. he has continued his teaching and consulting on inertial navigation and tracking for private industry, DOD, and university research.

Dr. Frank van Graas is a Fritz J. and Dolores H. Russ Professor of Electrical Engineering at Ohio University, where he has been on the faculty since 1988. He is an ION past president (1998- '99) and currently serves as the ION treasurer. He served as the ION's Executive Branch Science and Technology Policy Fellow at NASA (2008-2009 academic year). At Ohio University his research includes GNSS, inertial navigation, low-frequency signals, LADAR/EO/IR, surveillance and flight test. He is an ION Fellow and has received the ION's Kepler (1996), Distinguished Service (1999), Thurlow (2002), and Burka (2010) awards.

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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

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

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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Kalman Filter Applications to Integrated Navigation 2

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: The course is designed to follow Kalman Filter Applications to Integrated Navigation 1 and Inertial Navigation, and will also be of benefit to intermediate-level attendees who are familiar with filtering concepts and inertial navigation principles.

Integration of GPS with an Inertial Measurement Unit (GPS/IMU) is used to illustrate the application of Kalman Filtering to integrated navigation. The course starts with a brief summary of the Kalman Filter followed by the steps required to implement the filter, including the selection of the state variables, observability, error sources, sensor bandwidth, update rate, time synchronization, lever arm, and identification of the noise processes. At the conclusion of the course, participants should be able to understand the underlying principles that lead to the successful design and implementation of Kalman filters for integrated navigation applications.

The approach presented offers a major benefit of departure from other IMU/satnav integrations. Precise carrier phase observations one second apart provide streaming velocity for dead reckoning, yielding huge improvement in multiple aspects of performance (robustness, integrity, interoperability, immunity to belowmask ionospheric and tropospheric degradations, etc.). Flight-verified cm/sec velocity performance, including an instance of zero elevation above horizon, is shown. Of crucial significance, integration with a low-cost IMU is shown to be sufficiently dramatic to conclude that there is little reason not to use it.

Dr. James L. Farrell is an ION Fellow and author of over 80 journal and conference manuscripts. He authored Integrated Aircraft Navigation (Academic Press, 1976) and GNSS Aided Navigation and Tracking (2007). His technical experience includes teaching appointments at Marquette and UCLA, Honeywell, Bendix-Pacific, and Westinghouse in design, simulation, and validation/ test for modern estimation algorithms in navigation and tracking applications, and digital communications system design. As president and technical director of VIGIL INC. he has continued his teaching and consulting on inertial navigation and tracking for private industry, DOD, and university research.

Dr. Frank van Graas is a Fritz J. and Dolores H. Russ Professor of Electrical Engineering at Ohio University, where he has been on the faculty since 1988. He is an ION past president (1998- '99) and currently serves as the ION treasurer. He served as the ION's Executive Branch Science and Technology Policy Fellow at NASA (2008-2009 academic year). At Ohio University his research includes GNSS, inertial navigation, low-frequency signals, LADAR/EO/IR, surveillance and flight test. He is an ION Fellow and has received the ION's Kepler (1996), Distinguished Service (1999), Thurlow (2002), and Burka (2010) awards.

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