EE 621 Fundamentals of Kalman Filtering and Smoothing
This course examines the properties of linear model, least-squares estimation (recursive and batch), Singular-Value Decomposition (SVD), Best Linear Unbiased Estimation (BLUE), likelihood, Maximum-Likelihood (ML) estimation, multivariate Gaussian random variables, mean-squares estimation of random parameters, Maximum a Posteriori (MAP) estimation of random parameters, Expectation-
Maximization (EM) algorithm. Practical applications will be emphasized with a number of hands on MATLAB Programming exercises. Prerequisite: EE561.
Prerequisite
EE 561 (C or better)