Estimation Theory

Course content

Gauss-Markov process and stochastic differential equations. Bayesian estimation theory. Maximum likelihood, linear minimum variance and least-square estimations. Properties of estimators; error analysis. State estimation for linear systems, Kalman-Bucy and Wiener filters. Smoothing and prediction. Nonlinear estimation. Filter implementation. Applications to communication, control, system identification and biomedical engineering.