MAE 6760

Model-Based Estimation

Course Description

Course information provided by the Courses of Study 2023-2024.

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 Prerequisite: linear algebra, differential equations, undergraduate-level probability theory, MATLAB programming. Prerequisite or corequisite: MAE 4780/MAE 5780 or ECE 6210.

• 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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