Optimal State Estimation

Optimal State Estimation

Synopsis:

The instructor presents state estimation theory clearly and rigorously, providing the right balance of fundamentals, advanced material, and recent research results. After taking this course, the student will be able to confidently apply state estimation techniques in a variety of fields. The features of this course include:


  • A straightforward, bottom-up approach that begins with basic concepts, and then builds step-by-step to more advanced topics.
  • Simple examples and problems that require paper and pencil to solve. This leads to an intuitive understanding of how theory works in practice.
  • MATLAB®-based state estimation source code for realistic engineering problems. This enables students to recreate state estimation results and experiment with other simulation setups and parameters.

After being given a solid foundation in the fundmentals, students are presented with a careful treatment of advanced topics, including H-infinity filtering, unscented filtering, high-order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H-infinity filtering.

Key Topics:

  • Fundamentals (linear systems, probability, and least squares estimation)
  • Kalman filtering and Kalman smoothing
  • Kalman filter generalizations
  • H-infinity filtering
  • Nonlinear Kalman filtering
  • Other nonlinear approaches: unscented Kalman filtering and particle filtering

Who Should Attend:

This course will benefit engineers who want to estimate the states of a dynamic system. This includes electrical, aerospace, chemical, and mechanical engineers, but could also include engineers from other disciplines. This course will interest those who want to learn the basics, as well as those who want to learn about recent advances and research directions.

Course Information:

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


Course scheduling available in the following formats:


  • Course at Conference
  • On-site Course
  • Stand-alone/Public Course

Course Length: 2-3 days
AIAA CEU's available: yes

Outline

Course Outline

Part I  Introductory material
    1. Linear systems
    2. Probability and stochastic processes
    3. Least squares estimation
    4. State and covariance propagation

Part II  The Kalman filter
    5. The discrete-time Kalman filter
    6. Alternate Kalman filter formulations
    7. Kalman filter generalizations
    8. The continuous-time Kalman filter
    9. Optimal smoothing
    10. Additional topics in Kalman filtering

Part III  The H-infinity filter
    11. The H-infinity filter
    12. Additional topics in H-infinity filtering

Part IV  Nonlinear filters
    13. Nonlinear Kalman filtering
    14. The unscented Kalman filter
    15. The particle filter

Optimal State Estimation


Course Outline:


I. Introductory material
A. Linear systems
B. Probability and stochastic processes
C. Least squares estimation
D. State and covariance propagation

II. The Kalman filter
A. The discrete-time Kalman filter
B. Alternate Kalman filter formulations
C. Kalman filter generalizations
D. The continuous-time Kalman filter
E. Optimal smoothing
F. Additional topics in Kalman filtering

III. The H-infinity filter
A. The H-infinity filter
B. Additional topics in H-infinity filtering

IV. Nonlinear filters
A. Nonlinear Kalman filtering
B. The unscented Kalman filter
C. The particle filter

Materials

Optimal State Estimation

 

 

 

Course Materials:


Since course notes will not be distributed onsite, AIAA and your course instructor are highly recommending that you bring your computer with the course notes already downloaded to the course.

Once you have registered for the course, these course notes are available about two weeks prior to the course event, and are available to you in perpetuity.

Student will receive the required textbook “Optimal State Estimation” for this course. This textbook is included in the registration fee.

Each student will preferably have a PC with a copy of Matlab. The instructor has written many programs in Matlab to illustrate the material in this course – see http://academic.csuohio.edu/simond/estimation/ for details.

 

Course Materials

Since course notes will not be distributed onsite, AIAA and your course instructor are highly recommending that you bring your computer with the course notes already downloaded to the course. Once you have registered for the course, these course notes are available about two weeks prior to the course event, and are available to you in perpetuity.

Course Recommended Reading:

Book Name: “Optimal State Estimation”
Author: Dan Simon

Publisher: John Wiley & Sons, 2006

Price: $136.00

Purchase this book.

Please note that the reference listed above is not required to take this course.

Instructors

Optimal State Estimation

 

 

 

Course Instructor:


Dan Simon received his PhD from Syracuse University in Electrical Engineering. He has 14 years of industrial engineering experience, and has been a professor at Cleveland State University since 1999. He has over 70 publications in journals and conferences, and is the author of the textbook “Optimal State Estimation.”

 

Instructor

Dan Simon

Dan Simon received his PhD from Syracuse University in Electrical Engineering. He has 14 years of industrial engineering experience, and has been a professor at Cleveland State University since 1999. He has over 70 publications in journals and conferences, and is the author of the textbook “Optimal State Estimation” (John Wiley & Sons, 2006).

 

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