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Course 557: Inertial Systems, Kalman Filtering and GPS / INS Integration

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Course 557: Inertial Systems, Kalman Filtering and GPS / INS Integration

Instructor: Dr. Alan Pue, Johns Hopkins University, APL (Retired) and Mr. Michael Vaujin, Consultant
December 12-16, 2022, 9AM-4:30PM (Eastern Standard Time) EST, Taught REMOTELY| 3.0 CEUs | This course is also available for private group training. CEUs

$3299

 

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.)
Course: 557
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.

— Name Withheld,
Course: 557
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.

— Matthew Donn, US Navy
Course: 557
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.  

— Sean Stel, L3 Harris
Course: 557
Remote Course, Summer 2022

Dr. Pue’s teaching style is excellent. His ability to explain complex concepts by relating to real-life examples was very effective.

— Mark Darnell, GE
Course: 557
Patuxent River, MD

Both instructors were very knowledgeable and had great presence. The excitement on the topics of each instructor was very evident and made it easier for me to stay engaged.

— Cameron Little, US Navy
Course: 557
Patuxent River, MD

It was very engaging and helped me learn topics that could have been tough to understand otherwise…Everything seemed relevant to our line of work.

— US Military, Name Withheld Upon Request,
Course: 557
Remote Course, December 2021

I would definitely recommend this course to any of my colleagues in the navigation areas!

— Soren Knutson, BAE Systems
Course: 557
Remote Course, December 2021

It is easy to tell that this course is taught by passionate instructors, and that comes through both in their mastery of the subject material, and enthusiasm in presenting the subject matter in a concise and easy-to-follow manner. Despite the difficulty of the material, this course is one of the most well-taught courses I’ve had the pleasure of taking. I urge both of the instructors to keep teaching, as an instructor’s passion is instrumental in a student’s absorption of material. Needless to say, they both have passion in spades.

— Aaron Bruinsma, L3 Harris Wescam
Course: 557
Remote Course, November 2020

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. 

— Jennifer Kimmett, Parsons
Course: 557
Remote Course, November 2020

Instructors were awesome, very responsive and personable, very conversational.

— Michelle Greiner, JHU/APL
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