Kalman Filter Applications to Integrated Navigation 1

Time: Tuesday, September 26, 9:00 a.m. - 12:30 p.m.
Room: Room C126

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

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 Multi-Constellation GNSS Signals

Time: Tuesday, September 26, 9:00 a.m. - 12:30 p.m.
Room: Room C125

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

Course Level: Beginner to Intermediate

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, 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.

Dr. John W. Betz is a Fellow of The MITRE Corporation. He has contributed to the design of modernized GPS signals, including developing the binary offset carrier (BOC) modulation. He has also influenced many aspects of GNSS engineering, including international efforts to achieve compatibility and interoperability among GPS and other satnav systems. He is a Fellow of the ION and the IEEE, and has extensively served the ION in programs and in officer capacities, most recently as chair of the Satellite Division. He received the ION’s Burka and Thurlow Awards, the Satellite Division’s Kepler Award, the IEEE AES’s Carlton Award, and the IAIN’s Harrison Award. Dr. Betz served on the U.S. Air Force Scientific Advisory Board from 2004 to 2013, including chairing it for three years, and is a member of the National Space-Based Positioning, Navigation and Timing Advisory Board. He received his PhD in Electrical and Computer Engineering from Northeastern University.

Android Raw Measurements - Including All Constellations and AGC: Theory and Application

Time: Tuesday, September 26, 9:00 a.m. - 5:00 p.m.
Room: Room C121/C122

Note: This is a two part, all-day course (9:00 a.m.-12:30 p.m. and 1:30 p.m.-5:00 p.m.).

Registration fee:
$800 if registered and paid by August 25
$900 if payment is received after August 25

Course Level: Beginner to Intermediate

Google launched raw GNSS measurements availability to apps in the Android N operating system.

This means you can get Pseudoranges, Dopplers and Carrier Phase from a phone or tablet. In this daylong course, you will learn to access and use these raw measurements.

The tutorial is hands-on, we will bring phones for you to use. You will collect, view, and process raw-measurements. You will leave the class with the data, Google software tools, and the knowledge of how to use them.

The daylong class is comprised of the following parts:

1. The Android Software Stack. You will learn how data flows through the Android software stack. In this part of the course, you will open your laptops, and we will show you where online to find the definitions of the different data structures. You will learn which of these is available to you at the Application layer.
2. Updates to Android O. What are the new changes that you can expect to see with the release of our latest Operating System.
3. Description of the available data. We will review the data that is accessible by developers (i.e. you) in Android. This is the theoretical part of the class. We will review the definitions of the different types of GNSS measurements, their physical meaning, and how to use them for analysis and location. At the end of this section, we will provide Android N phones that you can use for the rest of the class.
4. Using the data. Collect GNSS measurements outside, you will download the data from the phones and do some processing. We will provide software tools that you can use during the class. The tools allow you to log data from an Android N or Android O devices, view the raw measurements, and do basic measurement analysis and position computation.
5. Finally, we will give you specific examples of research projects and applications that you can develop with the tools and knowledge obtained in the class. For example: how to build a GNSS data analysis app; how to build a crowd-sourced jammer detector; etc.

To tailor this tutorial to your own needs, visit this online form and let us know what you would like us to cover in the class: https://goo.gl/forms/ECXpobtEHMtbCbaC2

Wyatt Riley is a software engineer in Google’s Android Location & Context team. His focus is on GNSS, from the raw measurements to location, to fusion with other location technologies. Prior to Google, Mr. Riley worked on GNSS location estimation at Qualcomm, working with hardware, measurement and OS teams, and led the GNSS-Inertial Location software development in use on billions of devices today. He holds a M.S. in Astronautical Engineering from Stanford, a B.S. in Engineering from Harvey Mudd, and has authored or co-authored 46 patents across GNSS, network and inertial location.

Steve Malkos is the technical program manager in Google’s Android Location & Context Group. He manages all engineering aspects for Android’s Location and Context programs. Mr. Malkos is responsible for defining these initiatives within Google, which includes everything from GPS, Fused Location Provider (FLP), Android Sensor Hub, Sensors, and more. Mr. Malkos has been involved in the field of location now for over 13 years. Prior to his role at Google, he worked as an associate program management director at Broadcom, where he managed engineering teams within Broadcom’s GNSS software. He holds a B.S. in Computer Science from Purdue University currently holds 8 patents and has many more pending patents in the field of location.

Dr. Mohammed Khider is a software engineer in Google’s Android Location & Context team. Within Android, he is a member of a research team that works on improving positioning accuracy of mobile devices in challenging indoor and urban canyon environments. He received his PhD in Electrical Engineering with focus on "Multisensor based Positioning for Pedestrian Navigation" from the University of Ulm, Germany. Prior to his role at Google, he was a research associate at the Institute of Communication and Navigation at the German Aerospace Center (DLR) where he worked on various research projects related to positioning and navigation. Mohammed has been actively involved in the field of location and context for over 10 years. His research interests are navigation, multi-sensor fusion, mobility models, signal processing and context-aware services.

Kalman Filter Applications to Integrated Navigation 2

Time: Tuesday, September 26, 1:30 p.m. - 5:00 p.m.
Room: Room C126

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

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 enabled by a 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.

GNSS Error Characterization, Analysis, and Mitigation

Time: Tuesday, September 26, 1:30 p.m. - 5:00 p.m.
Room: Room C125

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

Course Level: Intermediate

This tutorial provides the attendee with an understanding of GNSS error characterization, analysis, and mitigation in various GNSS architectures and applications. The course begins with an overview of the various error components for GNSS and how their bias, variation, and rate may affect the overall user solution performance. Error characterization, isolation, analysis, and mitigation will be presented for stand-alone, multi-frequency, and differential-based GNSS architectures for various GNSS applications. Error considerations in various differential-based GNSS architectures will be discussed including correction and measurement-based, SBAS, PPP, and RTK architectures.

Course Outline:

• Error component characterization for GNSS
• Error budgets for various GNSS architectures and applications
• A GNSS signal model and error terms
• Truth position, velocity, and time references for error analysis
• Satellite orbit and clock errors
• Atmosphere errors
• Ionosphere errors (characterization, analysis, mitigation)
• Troposphere errors (characterization, analysis, mitigation)
• Multipath (code and carrier) errors (characterization, analysis, mitigation)
• Error mitigation by smoothing
• Code bias terms
• Error considerations in differential GNSS architectures and mitigation approaches (corrections and measurement-based, SBAS, PPP, RTK)

Dr. Chris G. Bartone, P.E. is a professor at Ohio University with over 30 years of professional experience. He received his PhD EE from Ohio University, a MSEE from the Naval Postgraduate School, and BSEE from Penn State. He previously worked for the Naval Air Warfare Center, performing RDT&E on CNS systems. Dr. Bartone has developed and teaches a number of GPS, radar, wave propagation and antenna classes. His research concentrates on all aspects of navigation.