Random Signals and Optimal Filtering

Course ID:  M1801U14

Credits: 3

Objective: Introduction to Random Signals and Kalman Filters.

Course Prerequisites: 

1.Matrix Operations

2.Probability and Random Variables

3.Linear Systems

Outline:

1.Probability and Random Variables

2.Mathematical Description of Random Signals

3.Response of Linear Systems to Random Inputs

4.Wiener Filtering

5.The Discrete Kalman Filter, State-Space Modeling, and Simulation

6.The Continuous Kalman Filter

7.Nonlinear Filtering - Linearized and Extended Kalman Filters

8.Applications - Navigation, Tracking

Teaching Method:  Lectures in Class

Reference:  

1.Gelb,A.,ed.,"Applied Optimal Estimation" ,MIT Press,1974.

2.Maybeck, S. P., "Stochastic Models, Estimation, and

Control", Vol. Ⅰ (1978), Vol. Ⅱ (1982), Academic Press.(Also has Vol.Ⅲ).

3.Grewal, M. S. and Andrews, A. P., "Kalman Filtering, Theory and Practice Using MATLAB", 2nd Ed., John Wiley & Sons, Inc., 2001.

4.Lewis, F. L., "Optimal Estimation", John Wiley & Sons, Inc., 1986.

5.Siouris, G. M., "An Engineering Approach to Optimal Control and Estimation Theory", John Wiley & Sons, Inc.