Course 557: Inertial Systems, Kalman Filtering and GPS / INS Integration
Instructor: Dr. Alan Pue, Johns Hopkins University, APL and Mr. Michael Vaujin, Consultant
Onsite & PublicOur most requested courses are offered at different public venues two to three times per year. Most of our courses also can be taught onsite at your location. Most on-site courses can be customized to your needs. Read more about our on-site course options.
July 13-17, 2020 $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 fulls 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 Carolyn McDonald 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.
Equipment You Should Bring
- A laptop (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, so this equipment is not required, but is recommended.
- These course notes are searchable and you can take electronic notes with the Adobe Acrobat Reader we will provide you.
Equipment You Should Bring
- A color electronic copy of all course notes will be provided on a USB Drive or CD-ROM. Bringing a laptop to this class is highly recommended; power access will be provided.
- A black and white hard copy of the course notes, printed 3 slides to a page.
- Public Venue Attendees: 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 on-site contracts. Any books for on-site group contracts are negotiated on a case by case basis.)
[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.
[My main objectives were to] increase new hires in our branch knowledge and awareness of Inertial Navigation and Kalman Filters. Goals were definitely met.
I’ve been writing Kalman Filters for many different types of systems for many years. I was hoping to fill in some gaps in the mathmatical theory, and did.
Dr. Pue kept in interesting. It’s easy to doze off in these type, but he kept it interesting.
I’ve filled in the math behind the Kalman Filter which will help me understand some of the finer points.
[The knowledge I acquired was] too much to list: IMU mechanism function, limitations, and considerations of navigation output from a Kalman filter, implementation strengths and weaknesses, when to correlate errors.
I appreciate the clear nomenclature definitions up front. This area has the potential to waste a lot of time if handled poorly. It was handled excellently in this course.