MAE 6760

Model-Based Estimation

Course Description

Course information provided by the 2025-2026 Catalog.

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.

Prerequisites Knowledge of undergraduate-level probability, linear algebra/linear systems, differential equations, or permission from the instructor.

Enrollment Priority Enrollment limited to: graduate students.

Last 1 Terms Offered 2025SP

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