ECE 563 Estimation Theory & Filtering
Parameter estimation for deterministic systems: least-squares, projection and persistent excitation methods. State and parameter estimation of stochastic systems. Bayesian estimation theory, maximum likelihood and maximum a-posteriori estimation. Optimal filtering. The Kalman recursive filter. Nonlinear estimation. Estimation bounds. Applications to communications and control.
Credit Hours: 3 Lecture
Prerequisites: ECE 551 or consent of instructor
Course Coordinator:
R. Viswanathan
Textbooks:
Lonnie C. Ludeman, "Random Processes : Filtering, Estimation, and Detection", John Wiley & Sons, 2003, ISBN: 0471-25975-6.
References:
M.D. Srinath, P.K. Rajasekaran, and R. Viswanathan, "Introduction to Statistical Signal
Processing With Applications", Prentice Hall, 1996, ISBN: 0-13-125295-x.
Goals:
To provide electrical and computer students with the ability to analyze estimation of parameters and waveforms in stochastic systems. -
To familiarize the students with the theory and analysis of Kalman filtering.
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To provide application examples in communications and control.
Projects:
Simulate Kalman filter for a linear model- example, radar target tracking. Simulation of a system identification example.
Computer Tools: Matlab Simulink
Last Review: Spring Semester 2004
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