Advanced Linear and Nonlinear Estimation

Course Description

This course will cover principles and techniques for designing, implementing, and analyzing linear and nonlinear state estimators for dynamical systems for which traditional least-squares and linear Kalman filtering approaches might not be sufficient. In particular, emphasis is placed on state space systems that are characterized by partial observability and/or non-Gaussian uncertainties that, generally, arise in applications governed by complex non-linear stochastic dynamics and measurement processes. First, a brief review of matrix theory, state-space models and realizations, probability theory, dynamic system motion models, least-squares estimation, Luenberger observers, and linear Kalman filters (continuous and discrete versions) is presented. Then, these concepts are extended to advanced state estimation concepts and applications, to include: extended Kalman filtering, unscented Kalman filters, Cubature and Cubature-Quadrature Kalman Filters, and particle filtering along with various application examples. Prerequisite(s): EN.525.609 Continuous Control Systems

Code

EN.525.763

Credits

3

Subject

Electrical and Computer Engineering

Programs That Include This Course

• Master's Electrical and Computer Engineering, Master of Science