Pre-conference tutorials have been organized to provide in-depth learning prior to the start of the technical program. All courses will be taught in a classroom setting. Electronic course notes will be made available for download by registered attendees from the conference website; registered attendees are encouraged to download notes in advance of the tutorials. Paper notes will not be provided. Power will not be available to course attendees for individual laptop computers; please come prepared with adequate battery power if required.

Tutorial registration is required for each individual course. ION reserves the right to cancel a tutorial based on inadequate registration or the unavailability of the instructor. Registration for tutorials is accomplished online through the normal conference registration process.

Tutorial Registration Rates:
Before March 20: $400 per course unit
After March 20: $450 per course unit

Monday, April 20: 9:00 a.m. - 12:30 p.m.
Contemporary and Emerging Inertial Sensor Technologies
Dr. Ralph Hopkins
Alternative Navigation Methods Exploiting Integration with Inertial Measurements
Dr. Maarten Uijt de Haag
Monday, April 20: 1:30 p.m. - 5:00 p.m.
Introduction to Inertial Navigation Systems, Kalman Filtering and Integrated Navigation
Dr. Andrey Soloviev
Fundamentals of Nonlinear Recursive Estimation
Dr. Michael J. Veth

Contemporary and Emerging Inertial Sensor Technologies

Time: Monday, April 20, 9:00 a.m. - 12:30 p.m.

This course will present an overview of current state-of-the art inertial instrument technology and how emerging developments in nano and micro-scale fabrication, solid-state optics and cold atom technologies are influencing gyroscope and accelerometer design. The course will initially focus on the recent developments in MEMS-based inertial instruments and how MEMS technology is revolutionizing the inertial guidance navigation and control (GN&C) industry. Current industry trends will be discussed along with examples of MEMS inertial technology in the commercial, military and space sectors, including advanced systems, which integrate inertial MEMS with GPS.

New developments in inertial instrument design will follow with discussion of how advanced nano and microfabrication methods, new solid state optical component developments and cold atom interfereometry are being exploited in the next generation of precision gyro and accelerometer designs.

Suitable for experienced inertial instrument practitioners, it will also be of interest to novice developers as it will cover an overview of basic inertial sensing principles, and detailed discussion of gyroscope and accelerometer designs. This course will appeal to R&D, systems and manufacturing engineers, managers and executives, and will conclude with a discussion on the future direction of advanced inertial technologies.

Course Outline

  • Overview of Inertial Sensing
  • MEMS Accelerometers and Gyroscopes
  • Emerging Inertial Sensors
  • Emerging INS Applications and Integration
  • Future Direction of Inertial Technology

Dr. Ralph Hopkins Ralph Hopkins is a Distinguished Member of the technical staff and group leader in the Positioning Navigation and Timing (PNT) Division at Draper where he is responsible for the design and development of inertial instruments and sensors. Ralph has served as technical director of advanced inertial instrument development programs including strategic, navigation and tactical grade gyroscopes and accelerometers. He is an invited speaker for short course tutorials on inertial instruments and inertial technology and has presented internationally for the NATO Science Technology Organization sponsored lecture series and symposia on navigation technology.




Alternative Navigation Methods Exploiting Integration with Inertial Measurements

Time: Monday, April 20, 9:00 a.m. - 12:30 p.m.

This tutorial provides an introduction to the latest technology trends for navigating in difficult urban and indoor environments where the performance of typical Global Navigation Satellite System (GNSS) receivers is deteriorated or absent. This introduction will shortly discuss three broad categories of alternative navigation (Alt-Nav) techniques including image/Ladar/Doppler/dead-reckoning aiding of inertial sensors, beacon-based navigation (including pseudolites), and navigation using signals-of-opportunity such as WiFi signals. Then the course will focus on the latest alternative navigation technologies based on electro-optical techniques specifically. The Alt-Nav technologies presented include laser- and image-aided INS and Simultaneous Localization and Mapping (SLAM) methods using laser and imaging sensors. In the former methods, tight integration with an INS should lead to navigation performance similar to that achieved in today’s GPS/INS integrations. The discussion includes the basic principles of integration with an IMU; EO/IMU integration mechanizations; the use of correlation techniques, feature-based techniques or optical-flow-based techniques; the use of a priori information such as terrain and feature databases; and SLAM approaches.

Course Outline:

  • Introduction to alternative navigation
  • Alternative navigation categories
  • Basic principles of integration with an IMU
  • EO/IMU integration mechanizations
  • Correlation techniques, feature-based techniques or optical-flow-based techniques
  • Use of passive and active electro-optical sensors to aid the inertial
  • Passive EO sensors: image-based navigation using features
  • Active EO sensors: ladar-based navigation using correlation and feature based techniques
  • Simultaneous Localization and Mapping approaches
  • Integration of image-based and Ladar-based sensors

Dr. Maarten Uijt de Haag Dr. Maarten Uijt de Haag is a Professor in the Institute of Aeronautics and Astronautics at the Technical University of Berlin (TU Berlin) where he leads the Chair for Flight Guidance and Air Traffic. Before this, Dr. Uijt de Haag was Professor at Ohio University and a Principal Investigator (PI) with its Avionics Engineering. He obtained his M.S.E.E. from Delft University and a Ph.D. in Electrical Engineering from Ohio University. He has authored or co-authored over 180 navigation-related publications and eight book chapters, is a senior member of the IEEE, a member of the ION, an Associate Fellow of the AIAA, and an Associate Fellow of the Royal Institute of Navigation. Dr. Uijt de Haag received the ION’s 2008 Thomas L. Thurlow Award for contributions to laser-based navigation and integrity monitors for synthetic vision systems.




Introduction to Inertial Navigation Systems, Kalman Filtering and Integrated Navigation

Time: Monday, April 20, 1:30 p.m. - 5:00 p.m.

This course introduces the main principles of integrated navigation, without overloading the participant with underlying mathematical complexity. A detailed explanation of integrated navigation system design can be quite overwhelming at first. Yet, it is possible to introduce simpler implementation examples in order to explain key concepts. The course begins with the simplest one-dimensional (1D) inertial mechanization (integration of acceleration into velocity and position) and will gradually progress through 2D and 3D INS cases and review of Kalman filtering to an integrated filter for range-domain sensor-fusion. Key concepts will be illustrated with Matlab-based simulation examples. While the course will not consider advanced topics, simplified mechanizations offered in this course are generally still applicable for cases of low-cost inertial sensors (such as consumer-grade MEMS) where higher-order effects (such as, for example, Coriolis effect) stay below the level of sensor errors.

Main topics will include:

  • Strap-down inertial navigation: from the simplest 1D case to a 3D system mechanization.
  • Introduction to Kalman filtering:
    • Kalman filter as a recursive formulation of one-dimensional averaging;
    • Extension to multi-dimensional cases;
    • Modeling system dynamics and trade-offs between system noise and measurement noise; and
    • Range-domain complementary Kalman filter for the INS-based sensor fusion.

Dr. Andrey Soloviev Dr. Andrey Soloviev is a principal at QuNav where he works on a variety of navigation mechanizations for GNSS-degraded and GNSS-denied environments. His research focuses on all aspects of multi-sensor fusion and GNSS signal processing for navigation applications. He is a recipient of the ION Early Achievement Award.




Fundamentals of Nonlinear Recursive Estimation

Time: Monday, April 20, 1:30 p.m. - 5:00 p.m.

This course presents an overview of estimation techniques suitable for systems with nonlinearities that are not well suited to traditional linear or extended Kalman filter algorithms. The course begins with an overview of the generalized recursive estimation problem and associated notation and conventions. Next, the limitations of applying linear theory to nonlinear problems are addressed, along with techniques for compensating for these adverse effects, including a brief overview of the traditional extended Kalman filter and Gaussian sum techniques. In addition, the mathematical effects of system nonlinearities on random processes are presented and discussed along with computational techniques for efficiently capturing this information, which serves as the foundation for the development of many nonlinear estimators. Next, the unscented Kalman filter (UKF) and particle filters (PF) are presented and analyzed using multiple examples. Common limitations of nonlinear estimators are addressed and hybrid solutions are discussed including Rao-Blackwell marginalization approaches.

The course concludes with a discussion and qualitative comparison of the strengths and weaknesses of various recursive estimation techniques from linear Kalman filtering to particle filtering, and their applicability to various problem spaces. Numerous Matlab examples are presented to illustrate sample nonlinear estimation algorithms and performance.

This course will be presented at an engineering level with the goal of understanding the fundamental concepts behind current nonlinear estimation algorithms and how they compare to traditional approaches. The course is appropriate for engineers and scientists with linear and extended Kalman filter experience with an interest in the potential benefits of nonlinear estimation algorithms for difficult problems. The “Fundamentals of Kalman Filtering” sequence by Dr. Gewal is a recommended prerequisite for students wishing to enhance their background prior to this course.

Dr. Michael J. Veth Dr. Michael J. Veth, Ph.D., is the president and CEO of Veth Research Associates. He leads a team of engineers dedicated to pursuing novel solutions in autonomous navigation, control, and tracking applications for GNSS-denied and degraded environments. He received his BSEE from Purdue University and a Ph.D. in Electrical Engineering from the Air Force Institute of Technology. He is a member of the ION, a Senior Member of the IEEE, and a graduate of the US Air Force Test Pilot School.