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The American Institute of Aeronautics and Astronautics (AIAA)

is the world's largest technical society dedicated to the global aerospace profession.

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    Course Overview

    Satellite Attitude Estimation

    Synopsis:

    This course will provide a standalone overview of attitude determination methods for spacecraft. The course will start with a review of attitude parameterization methods, attitude kinematics, and attitude dynamics. Then deterministic attitude determination methods will be discussed, such as TRIAD, QUEST, Markley’s SVD and FOAM algorithms, and Mortari’s ESOQ and ESOQ2. Probability and Stochastic Processes material will be reviewed in order to create a foundation for stochastic state estimation methods. Kalman filtering theory will be reviewed, as well as the extension of this theory to nonlinear dynamic systems – the extended Kalman filter (EKF). This theoretical foundation will be used to develop an extended Kalman filter for attitude determination. More advanced filtering methods, including, e.g., the sigma point filter and particle filtering methods, will be addressed if time permits.

    Key Topics:

    • Review of attitude parameterization
    • Review of attitude kinematics and dynamics
    • Deterministic attitude determination methods
    • Review of Probability, Stochastic Processes, and Kalman filtering theory
    • Application of Kalman filtering theory to attitude estimation
    • Advanced filtering concepts (time permitting): sigma point filtering, particle filtering

    Who Should Attend:

    This course is intended for engineers in the space industry, primarily those involved in satellite development or operations, and for researchers and graduate students wanting a quick exploratory start in the subject. Most of the material will be presented at a survey level, but some topics will be covered at a deeper level. Example applications will be presented, to illustrate and motivate the theory and algorithms. Basic knowledge of linear algebra, linear systems theory, satellite dynamics, and probability theory will be beneficial, although the course will be designed to be self-sufficient. Working knowledge of Matlab will be beneficial.

    Course Information:

    Type of Course: Instructor-Led Short Course
    Course Level: Intermediate


    Course scheduling available in the following formats:


    • Course at Conference
    • Onsite Course
    • Stand-alone/Public Course

    Course Length: 2 days
    AIAA CEU's available: yes