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.
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.)
