Course 557: Inertial Systems, Kalman Filtering and GPS / INS Integration
Instructor: Dr. Alan Pue, Johns Hopkins University, APL (Retired) and Mr. Michael Vaujin, Consultant
April 14-18 & December 8-12, 2025 | 9:00-4:30 EST | 3.0 CEUs | This course is also available for private group training. CEUs
$3299
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A Message from the Course Instructors
Course Description
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. Student discounts available for select public courses. See registration form for details.
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.
Prerequisites
- 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 Equipment
- 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.)
Remote Presentation, Fall 2024
Vaujin is a great instructor and very engaging. I could take an entire semester course from him. I really enjoyed going through the Matlab with him, and the hands-on was where I was able to pick up the most knowledge.
Remote Presentation, Fall 2024
My main objective was to get more familiar with the design of Navigators. I have seen a lot of the material implemented in Simulink but have never done a dive into the mathematics behind them. This course definitely met my main objective and more.
Remote Presentation, Spring 2024
Alan’s teaching style was exceptionally good. He obviously knows the material thoroughly. He starts with simple concepts and simple mathematical equations and then builds on them in a very systematic manner time and time again using the same notation and the same variables all along the way. He really pulled everything together in a very cohesive and understandable way.
Remote Presentation, Spring 2024
I really enjoyed Mike’s teaching style, he did a great job with making complex topics digestible and was very knowledgeable about practical estimation. He did a great job fielding questions and giving well-reasoned and understandable answers. I’ve been working with Extended Kalman Filters for a few years now and feel like I have a solid understanding of what they do and how they work, and even so I found I deepened that foundation with the way he explained things.
Remote Presentation, Spring 2024
I would absolutely recommend (Course 557) to my colleges. I work in the area of navigation. I think anyone that is going to work in that area over their career would benefit from this course.
Remote Presentation, Fall 2023
As a student, I really appreciate well-documented code that implements topics discussed in class. Having the code for Mr. Vaujin’s portion is awesome
Remote Presentation, Spring 2023
Yes, Alan was very effective, mastered the material and explained complex topics thorough.
Remote Course, Summer 2022
Michael is very well-versed and knowledgeable in the field of navigation, and his decades-long experience shows up in his presentation of the topic. I liked that he is able to zoom straight into the crux and motivation of the various GPS/INS techniques as well as share candidly on the practical implementation details. The Matlab examples and codes provided definitely helped in my learning.
Remote Course, Summer 2022
Mike is an energetic lecturer and his many real-life examples were interesting and informative. The discussion on the different types of Kalman filters, how they differ from each other, pros and cons, and of course the sample Matlab code should prove extremely useful.
Remote Course, Summer 2022
Dr. Pue’s lectures were effective in helping me to understand the material. He did a good job of customizing the lecture for the audience based on the questions he was receiving. I can’t recommend this excellent course enough to my colleagues who work with Inertial Navigation Systems. Â