Statistical Signal Processing
Course Description
This course is devoted to cover the principles of statistical processing, discussing aspects of statistical characterization of random signals, main aspects of estimation and detection theories as well as other important topics such as filtering and prediction and recursive (adaptive) implementations. The mathematical fundamentals are of major interest but the algorithmic procedures are also of interest.
Prerequisites
Knowledge of calculus, linear algebra, statistics and signal processing is required.
Course Goal
To provide students with the concepts of statistical modeling of signals and systems and to work in relevant problems of parameter estimation, signal detection and other signal processing approaches which exploit the statistics of the random signals, such as filtering, prediction and recursive implementations.
Program / Syllabus
- Review on probabilistic models
- Second order moments analysis
- Estimation theory
- Optimum filtering
- Prediction of stationary signals
- Detection theory
- Recursive methods and algorithms
Textbooks
- Robert M. Gray and Lee D. Davisson, An Introduction to Statistical Signal Processing, Cambdrige University Press, 2004.
- Dimitris Manolakis, Vinay K. Ingle, and Stephen M. Kogon, Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing (Artech House Signal Processing Library). Artech House Publishers, 2005.
- Mourad Barkat, Signal Detection and Estimation (Artech House Radar Library). Artech House Publishers, 2nd edition, 2005.
Material (in Portuguese)
