Course 557: Inertial Systems, Kalman Filtering and GPS / INS Integration
Instructor: Dr. Alan Pue, Johns Hopkins University, APL (Retired) and Mr. Michael Vaujin, Consultant
This course is also available for private group training | 3.0 CEUs
May 17 - 21, 2021, 9AM-4:30PM EST | $3299
This 5-day course on GPS-aided navigation will thoroughly immerse you in the fundamental concepts and practical implementations of the various types of Kalman filters that optimally fuse GPS receiver measurements with a strapdown inertial navigation solution. The course includes the fundamentals of inertial navigation, inertial instrument technologies, technology surveys and trends, integration architectures, practical Kalman filter design techniques, case studies, and illustrative demonstrations using MATLAB®.
Five full days allow for a full and detailed development of the design of an aided navigation system, combined with a detailed discussion of the use of lower quality IMUs, and advanced filtering techniques.
Mathematics Review. Note: The first three hours of the course includes a review of the mathematical equations needed for this course. If you do not need the review and want to opt out of the Monday morning session, please contact Trevor Boynton to register separately for the course at a slightly reduced fee.
- Familiarity with principles of engineering analysis, including matrix algebra and linear systems.
- A basic understanding of probability, random variables, and stochastic processes.
- An understanding of the GPS operational principles in Course 356, or equivalent experience.
Who Should Attend?
- GPS/GNSS professionals who are engineers, scientists, systems analysts, program specialists and others concerned with the integration of inertial sensors and systems.
- Those needing a working knowledge of Kalman filtering, or those who work in the fields of either navigation or target tracking.
- Recommended, but not required: A computer (PC or Mac) with full version of MATLAB 5.0 (or later) installed. This will allow you to work the problems in class and do the practice "homework" problems. However, ALL of the problems will also be worked in class by the instructor.
- These course notes are searchable and you can take electronic notes with the Adobe Acrobat Reader we will provide you.
Materials You Will Keep
- A color electronic copy of all course notes provided in advance on a USB drive or CD-ROM.
- Ability to use Adobe Acrobat sticky notes on electronic course notes.
- NavtechGPS Glossary of GNSS Acronyms.
- A black and white hard copy of the course notes.
- Textbook: Introduction to Random Signals and Applied Kalman Filtering, 3rd edition, by R. Grover Brown and Patrick Hwang, John Wiley & Sons, Inc., 1996. (Note: This does not apply to private group contracts. Any books for group contracts are negotiated on a case by case basis.)
Some of the different Kalman filtering techniques, such as the fixed point filter can be put to use in our work. The MATLAB navigator/Kalman filter examples are very useful.
Instructors were awesome, very responsive and personable, very conversational.
Remote Course, July 2020
Can’t speak highly enough. He was engaging and taught the course like a veteran IMU designer teaching novices what to do when they design their own IMU. Do this trick of the trade. Watch out for that pitfall. Be explicit with your vector notation. All of those things Vaujin did and I felt were immensely helpful.
Remote Course, July 2020
My main objectives were to learn the aspects of and how to implement modern navigation algorithms. The course definitely met those objectives.
I have recently become interested in learning about strapdown navigation. My objective was to increase my exposure to the topic and gain a more solid overview. This course met and exceeded my objectives
My goal for the course taught by Dr. Alan Pue was to understand how IMUs were integrated into the Kalman filter. The second day’s lecture way very helpful for that purpose.
[My main objective was to] gain a basic understanding of navigation systems; this course exceeded this expectation with a lot of very in-depth information.
I found everything to be useful. The review of filtering concepts was a great refresher and filled several holes in my knowledge. I’ve not done any GPS work before, so learning about how to integrate them into a navigation system was imminently practical.
My primary objective was to gain an understanding of the physics behind inertial navigation systems as well as learn how to build a robust Kalman Filter and implement it. I did learn what I wanted to from this course.
Understanding basic terminology is helpful for me just because I don’t work with Nav analysis daily. I’m about to start doing more on the Nav team and I think I have a good understanding of how to handle errors and not blindly use current Kalman Filter functions here.