Tuesday, September 17: 9:00 a.m. - 12:30 p.m.
Multi-constellation GNSS Signals and Systems	Dr. Chris G. Bartone, P.E.
Fundamentals of Inertial Navigation Systems and Aiding	Dr. Michael Braasch
Kalman Filter Applications to Integrated Navigation 1	Dr. James L. Farrell / Dr. Frank van Graas
Tuesday, September 17: 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
Kalman Filter Applications to Integrated Navigation 2	Dr. James L. Farrell / Dr. Frank van Graas

Multi-constellation GNSS Signals and Systems

Time: Tuesday, September 17, 9:00 a.m. - 12:30 p.m.
Room: Monroe

Registration fee:
$400 if registered and paid by August 16
$450 if payment is received after August 16

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.

---

Fundamentals of Inertial Navigation Systems and Aiding

Time: Tuesday, September 17, 9:00 a.m. - 12:30 p.m.
Room: Tuttle

Registration fee:
$400 if registered and paid by August 16
$450 if payment is received after August 16

Course Level: Beginner

This tutorial will start by highlighting the basic principles of operation of an inertial navigation system. The course will focus initially on the concepts underlying the algorithms used to determine position, velocity and attitude from inertial sensor measurements. Key error characteristics will be described as well such as Schuler oscillation and vertical channel instability. We will also consider the impact of various sensor errors on system performance. The tutorial will continue by covering the basics of Kalman filtering and aided-inertial systems. The daunting matrix mathematics involved in the full algorithm can be extremely intimidating to the newcomer. The basic concepts of estimation theory will be briefly reviewed, and the Kalman Filter will be described first in terms of simple one-dimensional problems for which the full algorithm reduces to an approachable set of scalar equations. We will look at the performance of the filter in some simple case studies and by the end will have an intuitive feel for how the full filter operates. We will apply the Kalman filter to the aiding of inertial systems. We will see how external sources of position and velocity (such as GPS) can be used first to measure inertial system error and then, with the aid of the Kalman filter, to estimate and correct inertial sensor error as well as system error.

Dr. Michael Braasch is the Thomas Professor of Electrical Engineering and a principal investigator with the Ohio University Avionics Engineering Center. Mike has over 30 years of experience in navigation research; and has taught graduate-level courses in inertial navigation, Kalman filtering and integrated navigation for the past 20 years. Dr. Braasch has also taught short courses on these subjects at all of the major inertial navigation system manufacturers in the United States. Dr. Braasch is an ION Fellow, a Senior Member of the IEEE and is an instrument-rated commercial pilot.

---

Kalman Filter Applications to Integrated Navigation 1

Time: Tuesday, September 17, 9:00 a.m. - 12:30 p.m.
Room: Flagler

Registration fee:
$400 if registered and paid by August 16
$450 if payment is received after August 16

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.

---

Introduction to Multiband and Multi-constellation SatNav Receivers using Python

Time: Tuesday, September 17, 1:30 p.m. - 5:00 p.m.
Room: Monroe

Registration fee:
$400 if registered and paid by August 16
$450 if payment is received after August 16

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. For those who attended the course last year, this course builds on that initial version and adds multiband (L1/E1, L5/E5a) and multi-constellation (GPS, Galileo) support. 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 a functional satnav software receiver running on their laptop that takes a multiband live-sky sampled data file, acquires and tracks visible open GPS and Galileo signals and outputs signal observables (i.e., carrier-to-noise-density ratio(C/N0) , accumulated Doppler range, uninitialized pseudorange). This open-source code may be further developed to yield a fully-functional satnav SDR that is ideal for research.

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 GPS and Galileo signals, including the new L1C signal
• Inter-frequency aiding
• Duty cycling for low-power mobile applications
• Tracking channel state machines
• Measurement computation
• Effects of fixed-point processing on tracking performance and measurement accuracy
• Performance improvement with Python multiprocessing

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. 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.

---

Autonomous System Navigation and Machine Learning

Time: Tuesday, September 17, 1:30 p.m. - 5:00 p.m.
Room: Tuttle

Registration fee:
$400 if registered and paid by August 16
$450 if payment is received after August 16

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, 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.

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.

---

Kalman Filter Applications to Integrated Navigation 2

Time: Tuesday, September 17, 1:30 p.m. - 5:00 p.m.
Room: Flagler

Registration fee:
$400 if registered and paid by August 16
$450 if payment is received after August 16

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.

---