Parameter and State Estimation

4 Units.

Bayesian and frequentist approaches to parameter and state estimation. Properties of estimators. Linear Gaussian systems and the Kalman Filter. Nonlinear state estimation, extended and unscented Kalman Filters. Introduction to Monte Carlo methods and particle filtering. Application to tracking and navigation.