MAE 6760

Model-Based Estimation

Course Description

Course information provided by the Courses of Study 2024-2025.

Course covers a variety of ways in which models and experimental data can be used to estimate model quantities that are not directly measured. Covers methods for solving the class of inverse problems that take the following form: given partial information about a system, what is the behavior of the whole system? Main estimation methods presented are batch least-squares-type estimation for general problems and Kalman filtering for dynamic system problems. Course deals with the issue of observability, which amounts to a consideration of whether a given inverse problem has a unique solution, and briefly covers the concept of statistical hypothesis testing. Techniques for linear and nonlinear models are taught. Both theory and application are presented.

When Offered Spring.

Permission Note Enrollment limited to: graduate students.
Prerequisites/Corequisites Knowledge of undergraduate-level probability, linear algebra/linear systems, differential equations, or permission from the instructor.

• Students will be able to create, run, interpret and analyze model based estimators such as the Kalman Filter, Extended Kalman Filter, Sigma Point Filter, Information Filter, Particle Filter, and Gauss Sum Filter.
• Students will be able to understand the strengths, weaknesses and best problems/applications for each filter.
• Students will be able to assess the accuracy of filters via statistical hypothesis tests.
• Students will be able to create a square root formulation of a filter for real time implementation.
• Students will be able to develop and analyze a model based filter for a self-selected problem/application.

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