PLANS 2012 Tutorial Schedule
All courses will be taught by leading GNSS educators in a classroom setting at the conference hotel. Paper course notes will be provided to attendees by the instructor the day of the course. Electronic notes may be made available at the discretion of the instructor.
Tutorial Registration Rates:
Before March 30 | After March 30 | |
|---|---|---|
One half-day session | $325 | $375 |
Two half-day sessions | $600 | $700 |
Time |
Course |
Instructor |
|---|---|---|
|
Dr. Dorota Grejner-Brzezinska |
|
Dr. Mohinder Grewal |
||
Mr. Ralph E. Hopkins |
||
Dr. Christopher Hegarty |
||
|
Dr. Chris Bartone |
|
Dr. Mohinder Grewal |
||
Alternative Navigation Methods Exploiting Integration with Inertial Sensors |
Dr. Maarten Uijt de Haag |
|
Nonlinear Estimation Techniques for Aided Navigation Systems |
Dr. Michael Veth |
Sensor Integration for Personal Navigation
Dr. Dorota Grejner-Brzezinska
Monday, April 23, 2012, 8:00 a.m. - 12:00 p.m.
This short course provides a review of the navigation sensors and techniques suitable for personal and pedestrian navigation, including selected Artificial Intelligence (AI) methods. Personal navigation (PN) is defined as navigation for military and emergency personnel, while pedestrian navigation refers to location/navigation/tracking of all other types of mobile users. The core technologies in personal navigation systems are the Global Positioning System (GPS) and inertial sensors, as well as other navigation sensors, to facilitate navigation indoors or in GPS-denied environments. An overview of some of these technologies, such as: wireless local area networks; IR and RF transponders; and ultra-wideband is provided. Following the technology overview, an example design that includes implementation and performance assessment of a personal navigation system prototype will be presented. This example design will be of a system that uses GPS, inertial measurement unit (IMU), digital barometer, magnetometer compass, and human locomotion model to provide a position estimate to support navigation and tracking of military and rescue ground personnel. A dead reckoning (DR) navigation approach will be presented that employs a human locomotion model using an adaptive knowledge-based system (KBS) based on AI techniques. The KBS is trained during the GPS signal reception, and then supports navigation under GPS-denied conditions. System design, as well as a summary of the performance analysis in the mixed indoor-outdoor environments, with the special emphasis on DR performance will be discussed.
Prerequisite:
This introductory level class that is suitable for engineers that are either new to or experienced in the field of personal navigation.
Course Outline:
- Basic terms and definitions used
- Need and motivation for personal/pedestrian navigation
- Primary research challenges
- Example of sensor configurations for pedestrian/personal navigation systems (including commercially available systems and research prototypes)
- Positioning techniques and methods used for personal navigation/localization
- Commonly used sensor integration methods/algorithms
- Example of personal navigation implementation based on Ohio State University solution
- Artificial intelligence methods supporting personal/pedestrian navigation including: Artificial Neural Networks and Fuzzy Logic; system design; and performance evaluation
- Summary and future trends (image-based navigation-basic concept and concept of collaborative navigation)
Biography:
Dr. Dorota A. Grejner-Brzezinska, Ph.D., is a professor in Geodetic Science, and director of the Satellite Positioning and Inertial Navigation (SPIN) Laboratory at The Ohio State University. Her research interests cover GPS/GNSS algorithms, in particular, high precision positioning and navigation, such as DGPS and RTK, GPS/inertial and other sensor integration for navigation in challenged environments, sensors and algorithms for indoor and personal navigation, signal processing in integrated navigation systems using Kalman filter and non-linear filtering, and precision orbit determination for GNSS/LEO. She is president of the International Association of Geodesy (IAG) Commission 4, Positioning and Applications, and IAG Fellow; she has been serving on the Institute of Navigation (ION) Council for the past eight years, and is an ION Fellow. She has published over 190 peer reviewed journal and proceedings papers, numerous technical reports and five book chapters on GPS and navigation, and led over 20 research projects sponsored by DOD, NASA, NGS, NGA, NSF, Federal DOT, Ohio DOT, with a total budget of over $13 million USD. She is the recipient of the 2005 ION Thomas Thurlow Award, the 2005 United States Geospatial Information Foundation (USGIF) Academic Research Award, and ESRI Award for Best Scientific Paper in Geographic Information Systems published in 2004. Her work on personal navigation, sponsored by NGA, was featured as “NGA success story” at the NGA NURI Symposium in Washington DC, in September 2008.
Fundamentals of Kalman Filtering for Navigation
Dr. Mohinder Grewal
Monday, April 23, 2012, 8:00 a.m. - 12:00 p.m.
This course discusses the fundamentals of Kalman filtering and its applications. The student will be provided with the analytical foundation necessary to implement a Kalman filter for a navigation application. In addition, the course addresses subtleties, problems, and limitations of estimation theory as applied to real world situations, and provides application examples. Attendees are encouraged (but not required) to bring a laptop with MATLAB®. Course emphasis is on intuitive concepts and practical applications, rather than rigorous mathematical presentation.
Prerequisite:
This tutorial will be presented at the engineering level and will provide the attendee with a basic understanding of Kalman filtering as it applies to inertial navigation. This is an introductory level class, in which the attendees are assumed to have a basic understanding of matrix theory, state-space representation of linear systems, and probability theory.
Course Outline:
- What is a Kalman filter
- Attributes of a Kalman filter
- Least square curve fit
- Discrete Kalman filter
- Continuous Kalman filter
- Discrete & continuous process noise
- Shaping filters
- Divergence
- Data rejection
- Measurements as scalars
- Examples of applications
- Practical considerations
Biography:
Dr. Mohinder S. Grewal, Ph.D., PE, has over 35 years of experience in systems identification, guidance, navigation, and control. He is well known for his innovative application of Kalman filtering techniques to real world modeling problems and his ability to communicate this complex subject to his students. Grewal co-authored Kalman Filtering Theory & Practice Using MATLAB, 3rd Edition, Wiley & Sons, 2008, and Global Positioning Systems, Inertial Navigation & Integration, 2nd Edition, Wiley & Sons, 2007. He has published more than 70 papers in IEEE and ION refereed journals and proceedings and holds patents in GUS clock steering and L1/L5 differential bias estimation. Dr. Grewal is professor of Electrical Engineering at California State University, Fullerton, which awarded him its 2008-2009 Outstanding Professor Award. His consulting associations include Raytheon Systems, Boeing Company, Lockheed Martin, University of California, Riverside, staff of the U. S. Department of the Interior, Geodetics, and Northrop Grumman. Grewal is a senior member of IEEE, Fellow of the Institute for the Advancement of Engineering, and member of the Institute of Navigation.
Contemporary and Emerging Inertial Sensor Technologies
Mr. Ralph E. Hopkins
Monday, April 23, 2012, 8:00 a.m. - 12:00 p.m.
This course will present an overview of current state-of-the art inertial instrument technology and how emerging applications of nanotechnology, solid-state optics and cold atom technologies are influencing gyroscope and accelerometer design. An overview of basic inertial sensing principles and a detailed discussion of gyroscope and accelerometer designs will be presented. The course will initially focus on the recent developments in Micro-electro-mechanical-system (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 Global Positioning System (GPS). New developments in inertial instrument design will follow with a discussion of the use of nanotechnology, new solid state optical component developments and cold atom interferometry in the next generation of precision gyro and accelerometer designs.
Prerequisite:
This course will appeal to Research and Development, systems and manufacturing engineers, managers and executives, and will conclude with a discussion on the future direction of advanced inertial technologies. This is an introductory level class suitable for novice developers and experienced inertial instrument practitioners.
Course Outline:
- Overview of Inertial Sensing
- Inertial MEMS Development
- MEMS Accelerometers and Gyroscopes
- System Application Examples
- Advanced Solid State Optical Instruments
- Emerging Inertial Applications of Nano and Cold Atom Technology
- Future Direction of Inertial Technology
Biography:
Ralph Hopkins is a Distinguished Member of the technical staff and group leader in the Guidance Hardware Division at Draper Laboratory where he is responsible for the design and development of inertial instruments and sensors. Mr. Hopkins has served as technical director of advanced inertial instrument development programs including strategic, navigation and tactical grade MEMS gyroscopes and accelerometers, and other high performance electro-mechanical inertial sensors. He holds four patents, has authored several papers and is an invited speaker for short course tutorials on inertial instruments and inertial technology. He has presented in Europe and the United Kingdom for the NATO Research and Technology Organization sponsored lecture series “Low Cost Navigation Sensors and Integration Technologies”. Mr. Hopkins holds a BS and ME in Mechanical Engineering from Rensselaer Polytechnic Institute, an ME in Engineering Mechanics from Columbia University, and an MS in Engineering Management from The Gordon Institute of Tufts University. He is a former member of the AIAA Guidance Navigation and Control technical committee.
An Introduction to the GPS Signals and Receiver Processing
Dr. Christopher Hegarty
Monday, April 23, 2012, 8:00 a.m. - 12:00 p.m.
This course provides an overview of digital modulation techniques used for satellite navigation systems. 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) are described. The course also provides a conceptual overview of GPS receiver signal processing, including a description of the basic techniques employed to acquire, track, and demodulate the navigation data from received GPS signals.
Prerequisite:
This tutorial is intended as an introductory course for GPS. This course will appeal to those new to the field as well as those experienced with GPS and are seeking to refresh or improve their knowledge of GPS.
Course Outline:
- Signal modulation for satellite navigation
- Direct sequence spread spectrum
- Autocorrelation, cross-correlation, polarization, and other signal characteristics
- Binary offset carrier and other modulation types
- GPS signals
- Modernization plans
- C/A, P(Y), L2C, L5, M-code, and L1C
- Signal generation, power levels, navigation data
- Receiver processing
- Functional overview
- Synchronization concepts – acquisition, code tracking, carrier tracking
- Data demodulation
Biography:
Dr. Christopher J. Hegarty, Ph.D., has been involved with aviation applications of GPS at MITRE’s Center for Advanced Aviation System Development since 1992. He is the chair of RTCA’s Program Management Committee, co-chair of RTCA Special Committee 159, and associate editor of NAVIGATION: The Journal of the Institute of Navigation. He was a co-recipient of the 1998 ION Early Achievement Award and the recipient of the ION’s 2005 Johannes Kepler Award. He served as ION president in 2008.
GPS Error Characterization, Mitigation, and Analysis
Dr. Chris Bartone
Monday, April 23, 2012, 1:00 p.m. - 5:00 p.m.
This course begins with a brief overview of Global Navigation Satellite Systems (GNSS) followed by a more detailed discussion of the Global Positioning System (GPS), how to generate a "stand-alone" GPS user solution and a description of a typical GPS error budget. The GPS error discussion will include error analysis, isolation and mitigation techniques. This course describes advanced error mitigation techniques, including differential GPS, and different ways to implement it, as well as, other error considerations for Precise Point Position (PPP) and high accuracy users. This course concludes with a discussion about future trends in GNSS. This course focuses on GPS, however, the methods and techniques presented in this class can be applied to other GNSS systems.
Prerequisite:
This tutorial provides the attendee with a basic understanding of GNSS and error characterization, mitigation, and analysis for GPS. This introductory to intermediate level class is suitable for those new to GNSS and to experienced GNSS engineers.
Course Outline:
- Overview of GNSS and GPS
- Overview of “stand-alone” GPS user solution and error budget
- Introduction of a GPS signal model and error terms
- Satellite orbit and clock errors
- Atmosphere errors: Ionosphere errors (characterization, mitigation, analysis); Troposphere errors (characterization, mitigation, analysis); Multipath (code and carrier) error (characterization, mitigation, analysis)
- Error mitigation by smoothing
- Error considerations for the PPP user
- Differential GPS (DGPS) and different ways to implement it
- Correction-based DGPS
- Relative-based DGPS
- A GPS error budget (DGPS)
- Error considerations for the high accuracy user
- Future Trends
Biography:
Dr. Chris Bartone, Ph.D.,P.E., is a professor at Ohio University. He received his Ph.D. in EE from Ohio University in 1998; he holds an MSEE from the Naval Postgraduate School (1987) and a BSEE from Penn State (1983). Dr. Bartone has 28 years of professional experience working with communications, navigation, and surveillance (CNS) systems. He worked for the Naval Air Warfare Center-Aircraft Division performing research and development on CNS systems. At Ohio University, Dr. Bartone has developed a number of graduate-level classes on GPS, radar, and wave propagation; his research concentrates on all aspects of navigation. He received the RTCA William E. Jackson Award in 1998 for his outstanding contributions to aviation in the area of DGPS. He is a member of the ION, IEEE, and the ILA. He has served the ION Council as air representative, eastern region vice president, and ION outreach chair. Currently he is editor of the ION Virtual Navigation Museum. He has helped organize many ION and IEEE conferences. Dr. Bartone is a licensed professional engineer and president of GNSS Solutions® Ltd.
Kalman Filtering for GPS/INS Integration
Dr. Mohinder Grewal
Monday, April 23, 2012, 1:00 p.m. - 5:00 p.m.
This course builds upon material covered in "Fundamentals of Kalman Filtering for Navigation with additional topics and navigation applications. The student will understand how to apply Kalman filtering to the navigation problem where Global Positioning System (GPS) is integrated with inertial navigation sensor to provide a optimal position estimate. The course addresses subtleties, problems, and limitations of estimation theory as applied to real world situations, and provides application examples. Attendees are encouraged (but not required) to bring a laptop with MATLAB®. Course emphasis is on intuitive concepts and practical applications, rather than rigorous mathematical presentation.
Prerequisite:
This tutorial will be presented at an engineering level with the goal of understanding the fundamental concepts behind GPS/INS integration. Attendees should have a basic understanding of generalized linear and extended Kalman filters or attend the "Fundamentals of Kalman Filtering" by Dr. Grewal. Dr Grewal's class is a morning tutorial at this conference.
Course Outline:
- Nonlinear Kalman Filters Sigma Point Kalman Filter (Unscented)
- Square Root Filtering
- Example with MATLAB®
- Nonlinearity Considerations
- GPS Measurement Models
- System Dynamics Models
- Examples
- Monitoring Filter Health
Biography:
Dr. Mohinder S. Grewal, Ph.D., PE, has over 35 years experience in systems identification, guidance, navigation, and control. He is well known for his innovative application of Kalman filtering techniques to real world modeling problems and his ability to communicate this complex subject to his students. Grewal co-authored Kalman Filtering Theory & Practice Using MATLAB, 3rd Edition, Wiley & Sons, 2008, and Global Positioning Systems, Inertial Navigation & Integration, 2nd Edition, Wiley & Sons, 2007. He has published more than 70 papers in IEEE and ION refereed journals and proceedings and holds patents in GUS clock steering and L1/L5 differential bias estimation. Dr. Grewal is professor of Electrical Engineering at California State University, Fullerton, which awarded him its 2008-2009 Outstanding Professor Award. His consulting associations include Raytheon Systems, Boeing Company, Lockheed-Martin, University of California, Riverside, staff of the U. S. Department of the Interior, Geodetics, and Northrop Grumman. Grewal is a senior member of IEEE, Fellow of the Institute for the Advancement of Engineering, and member of the Institute of Navigation.
Alternative Navigation Methods Exploiting Integration with Inertial Sensors
Dr. Maarten Uijt de Haag
Monday, April 23, 2012, 1:00 p.m. - 5:00 p.m.
This tutorial provides an introduction to the latest technology trends for navigating in difficult urban and indoor environments where typical Global Positioning System (GPS) receivers do not function. This tutorial will discuss three broad categories of alternative navigation (Alt-Nav) techniques including 1) image/LAser Detection And Ranging (LADAR)/Doppler/dead-reckoning aiding of inertial sensors, 2) beacon-based navigation (including pseudolites), and 3) navigation using signals-of-opportunity such as WiFi signals. The tutorial will then concentrate on alternative navigation technologies that utilize the latest electro-optical techniques. Specifically, the approaches used to implement integrated optically-aided inertial navigation systems (INS) and LADAR-aided INS, which can achieve performance similar to that achieved in today's GPS/INS integrations, will be discussed. The discussion includes the basic principles of integration with an IMU; Electro-Optic (EO)/ Inertial Measurement Unit (IMU) integration mechanizations; the use of correlation techniques, feature-based techniques or optical-flow-based techniques; and the use of a priori information such as terrain and feature databases. Finally three implementation approaches will be addressed in detail. They are 1) a feature-based passive image-based aiding method; 2) an active method using LADAR-based correlation and feature-based method with and without a priori information; and 3) an approach that combines both image-based and LADAR-based methods.
Prerequisite:
This tutorial will be presented at an engineering level with the goal of understanding the fundamental concepts behind the integration of passive and active optical systems with inertial sensors. The attendee should have a basic understanding of conventional GPS-aided inertial navigation systems.
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
- Integration of image-based and Ladar-based sensors
Biography:
Dr. Maarten Uijt de Haag, Ph.D. is a professor of Electrical Engineering and Computer Science and a principal investigator (PI) with the Avionics Engineering Center at Ohio University since 1999. He obtained his M.S.E.E. degree from Delft University in The Netherlands in 1994 and a Ph.D. in Electrical Engineering from Ohio University in Athens, Ohio in 1999. He has authored or co-authored over 100 navigation-related publications, including three book chapters. He is a senior member of the IEEE, an associate Fellow of the AIAA, and a member of the SPIE and ION. Within the latter organization, Dr. Uijt de Haag has served as the air representative on the Council, has co-edited the Institute of Navigation Red Book on “Integration Navigation Systems,” and is currently an associate editor for NAVIGATION. Dr. Uijt de Haag was awarded the ION’s 2008 Thomas L. Thurlow Award for his contributions to laser-based navigation and integrity monitors for synthetic vision systems.
Nonlinear Estimation Techniques for Aided Navigation Systems
Dr. Michael Veth
Monday, April 23, 2012, 1:00 p.m. - 5:00 p.m.
This tutorial 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 tutorial begins with an overview of the generalized recursive estimation problem and associated notation and conventions. Next the limitations associated with applying linear theory to nonlinear problems and techniques to compensate for these adverse effects are presented. Examples will be presented showing how these limitations are accommodated using a traditional extended Kalman filter and multiple model adaptive estimation (MMAE) techniques. The next section of the tutorial will describe the mathematical effects of system nonlinearities on random processes along with computational techniques to efficiently capture this information. This discussion serves as the foundation for the development of many nonlinear estimators. Next, the unscented Kalman filter (UKF) and particle filters (PF), including their limitations, are presented and analyzed. Multiple examples of non-linear estimators are provided. The tutorial concludes with a discussion and qualitative comparison of the strengths, weaknesses and applicability to various problem spaces of various recursive estimation techniques from linear Kalman filtering to particle filtering.
Prerequisite:
This tutorial 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. Attendees should have an understanding of linear and extended Kalman filters or attend the "Fundamentals of Kalman Filtering" by Dr. Grewal. Dr Grewal's class is a morning tutorial at this conference.
Course Outline:
- Introduction / Background
- Foundations of estimation theory
- Applying linear theory to nonlinear problems
- Compensating for inadequacy of system models
- Review extended Kalman filter, illustrate limitations
-
Nonlinear Filtering
- Multiple-model filtering
- Effects of nonlinear operations on random processes
- Expressing probability distribution functions (Sigma points & Particles)
- Unscented Transformation (Sigma-point filtering UKF)
- Particle filtering (Grid-methods, Sampling Importance Resampling (SIR) Filter)
- Hybrid filtering – addressing limitations (Unscented particle filtering conceptual overview & Rao-Blackwell particle filtering conceptual overview)
- Conclusion and Summary
- Continuum of estimation techniques – an engineering trade space
Biography:
Dr. Michael J. Veth, Ph.D., is currently the deputy director of the 46th Range Group, Eglin Air Force Base, Florida. Previously, he served as an assistant professor of Electrical Engineering at the Air Force Institute of Technology. His research focus is on applying advanced estimation theory to combine inertial sensors with non-traditional, bio-inspired sensors for non-GPS navigation and control applications. He received his Ph.D. and M.S. in Electrical Engineering from the Air Force Institute of Technology and a B.S. in Electrical Engineering from Purdue University. Dr. Veth has authored over 40 technical articles, presentations, and book chapters in areas relating to computer vision, navigation, and control theory. He is a member of the ION and a Senior Member of the IEEE. In addition, Dr. Veth is a graduate of the US Air Force Test Pilot School.