Introduction (1 week)
Signal estimation
State estimation
Least squares estimation
Stochastic theory (2 weeks)
Review of probability theory
Random variables and signals
Systems with random inputs
Optimal Estimation (1 and 1/2 weeks)
Maximum likelihood estimation
Maximum a posteriori estimation
Minimum mean square error estimation
Wiener Filtering (2 and 1/2 weeks)
FIR Wiener filter
Noncausal IIR Wiener filter
Causal Wiener filter
Kalman filtering (5 weeks)
Derivation of the Kalman filter
Filter properties
Steady-state Kalman filter
Prediction and smoothing
Applications
Nonlinear Filtering (2 weeks)
The extended Kalman filter
The Levenberg-Marquardt measurement update
Applications
