Tuesday, September 25: 9:00 a.m. - 12:30 p.m.
Kalman Filter Applications to Integrated Navigation 1	Dr. James L. Farrell / Dr. Frank van Graas
Introduction to Multi-Constellation GNSS Signals	Dr. Christopher J. Hegarty
Hands-on Introduction to GNSS Software Receivers and Signal Processing	Dr. Sanjeev Gunawardena
Tuesday, September 25: 1:30 p.m. - 5:00 p.m.
Kalman Filter Applications to Integrated Navigation 2	Dr. James L. Farrell / Dr. Frank van Graas
Introduction to BeiDou (BDS)	Dr. Mingquan Lu / Dr. Jade Morton
Autonomous System Navigation and Machine Learning	Dr. Mike Veth / Dr. Donald Venable

Kalman Filter Applications to Integrated Navigation 1

Time: Tuesday, September 25, 9:00 a.m. - 12:30 p.m.

Registration fee:
$400 if registered and paid by August 25
$450 if payment is received after August 25

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.

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

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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Introduction to Multi-Constellation GNSS Signals

Time: Tuesday, September 25, 9:00 a.m. - 12:30 p.m.

Registration fee:
$400 if registered and paid by August 25
$450 if payment is received after August 25

This course provides an overview of multi-constellation GNSS signals. Digital modulation techniques used for satellite navigation systems will be described, including a discussion of important characteristics such as pseudorandom noise codes, autocorrelation/cross-correlation properties, power levels, and polarization. Common features found in modern GNSS signal designs will be introduced, including dataless (pilot) components, square-wave subcarriers, secondary codes, forward error correction, and error detecting coding.

The present and future signals of the Global Positioning System (GPS), including C/A-code, P(Y)-code, L2 civil (L2C), L5, M-code, and L1 civil (L1C) will be detailed, as will the signals for GLONASS, GALILEO, BeiDou (BDS), satellite-based augmentation systems (SBAS), and other emerging satellite navigation systems.

This class is intended for anyone with an interest in better understanding multi-constellation GNSS signals, including researchers, design engineers, application developers, end-users, systems engineers, managers and executives. Attendees are assumed to have a familiarity with the basic concepts of satellite navigation.

Course Level: Beginner to Intermediate

Dr. Christopher J. Hegarty is the director for CNS Engineering & Spectrum with The MITRE Corporation. He is the chair of RTCA’s Program Management Committee, co-chair of RTCA Special Committee 159, and associate editor of NAVIGATION. He was the recipient of the 2005 ION Johannes Kepler Award, and served as ION president in 2008. He is a Fellow of the ION, the IEEE, and the co-author of Understanding GPS: Principles and Applications, 3rd Edition.

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Hands-on Introduction to GNSS Software Receivers and Signal Processing

Time: Tuesday, September 25, 9:00 a.m. - 12:30 p.m.

Registration fee:
$400 if registered and paid by August 25
$450 if payment is received after August 25

This course provides a “hands-on” approach in providing attendees with a solid understanding of the fundamentals of GNSS software receivers and associated signal processing. The course is divided into multiple modules; each is comprised of a short lecture followed by the completion of a software project that reinforces the concepts and techniques taught. By the end of the course, attendees will have a functional GNSS software receiver running on their laptop that takes a sampled GNSS data file, acquires visible GNSS signals, tracks a specified signal, and outputs signal observables (i.e., carrier-to-noise-density ratio(C/N0) , accumulated Doppler range, uninitialized pseudorange). This baseline open-source code may be further developed to yield a fully-functional GNSS software receiver that is ideal for research.

Topics covered:

• Precise modeling and simulation of GNSS signals, including C/N0, dynamics and code-carrier divergence
• FFT-based GNSS signal acquisition
• Correlation across satellite-referenced 1ms epochs on data blocks referenced to receiver 1ms epochs: the split-sum correlator
• Bit synchronization
• Carrier tracking loops: FLL, PLL and FLL-aided-PLL
• Code tracking loops: DLL, non-coherent vs. coherent tracking, correlator spacing and carrier aiding
• Tracking channel state machines
• Measurement computation

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 be providing information and software to registered attendees in advance of the course.

Course Level: Beginner to Intermediate

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 GNSS receiver design, advanced GNSS signal processing and implementation. He received the 2007 RTCA William E. Jackson Award for outstanding contribution to aviation for the application of transform-domain technology for high-fidelity GNSS performance monitoring. 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 2

Time: Tuesday, September 25, 1:30 p.m. - 5:00 p.m.

Registration fee:
$400 if registered and paid by August 25
$450 if payment is received after August 25

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.

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.

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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Introduction to BeiDou (BDS)

Time: Tuesday, September 25, 1:30 p.m. - 5:00 p.m.

Registration fee:
$400 if registered and paid by August 25
$450 if payment is received after August 25

This course will provide a comprehensive and in-depth introduction to the Chinese BeiDou Navigation Satellite System (BDS). As a member of the world’s GNSS family, BDS has many similarities with other GNSSs, such as GPS, GLONASS, and Galileo. However, BDS also possesses many unique features. Its uniqueness is not only reflected in the constellation structure, the control segment configuration, and the user terminals, but also in its signal structures, ranging and positioning methods, services, and applications. BDS even created a completely different development model from other GNSSs. These differences made BDS a unique and distinctive GNSS.

This course will begin with a brief overview and uniqueness analysis of many aspects of BDS, followed by a detailed introduction to BDS system architecture and service performance, signal structures and receiving schemes, and compatibility and interoperability with other GNSSs. While the course covers historic development in BDS, including BDS I, II and III, the focus will be on the emerging BDS III, especially on the new generation navigation signals and the latest experimental results. The course will conclude with a discussion on the status and future development and direction of BDS.

This course is designed for beginners who are not familiar with BDS, and GNSS experts seeking a deeper understanding of BDS. The target audiences are graduate students, R&D, systems and manufacturing engineers, managers and executives, and government agency staff in the fields of PNT and satellite navigation.

Course Level: Beginner to Intermediate

Dr. Mingquan Lu is a professor and director of the Positioning, Navigation and Timing (PNT) Research Center in the Department of Electronic Engineering at Tsinghua University, China. His current research interests include GNSS signal processing, receiver development, application security, and alternative PNT technologies. He is a member of the Expert Group of BDS.

Dr. Jade Morton is a professor in the Aerospace Engineering Sciences Department at the University of Colorado, Boulder (CU). Prior to joining CU, she was an electrical engineering professor at Colorado State University and Miami University. Her research interests lie at the intersection of satellite navigation technologies and remote sensing of the Earth’s space environment, atmosphere, and surface. She is the executive VP of ION, and a Fellow of IEEE and ION.

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Autonomous System Navigation and Machine Learning

Time: Tuesday, September 25, 1:30 p.m. - 5:00 p.m.

Registration fee:
$400 if registered and paid by August 25
$450 if payment is received after August 25

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. This short course will explore the concepts and technology associated with developing and testing navigation systems for autonomous vehicles, both using traditional multi-sensor fusion techniques and via 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, sensor integration approaches are developed including traditional Kalman filtering and proceeding to nonlinear filtering techniques. Comparisons are made regarding performance trade-offs for the various approaches.

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 navigation using a monocular camera sensor using open-source data.

Course Level: Intermediate

Dr. Mike Veth is the CEO 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 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. Donald Venable is an electronics engineer in the Navigation and Communications branch of the Air Force Research Laboratory Sensors Directorate. He earned his PhD in Electrical Engineering from the Air Force Institute of Technology. His research includes GPS-denied navigation, computer vision, machine learning, estimation, and enabling technologies.

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