This course explores the concepts and use of adaptive filtering algorithms and structures to learn the optimal filter or estimator and track timevarying system dynamics in order to improve the performance over static, fixed filtering techniques. Adaptive systems are analyzed as part of the coursework with application to digital communications, beamforming, control systems, and interference cancellation.
Some knowledge of probability and stochastic processes is required.
To provide students with the ability to apply adaptive filtering techniques to real-world problems (e.g. adaptive interference cancellation, adaptive equalization) in order to improve the performance over static, fixed filtering techniques. To provide a theoretical basis of adaptive signal processing necessary for the students to extend their area of study to additional applications, and other advanced concepts in statistical signal processing.
Program / Syllabus
- Motivation and Introduction
- Review on Stochastic Processes
- Linear Optimum Filtering
- Steepest Descent Gradient: Search Methods
- Least Mean Squares (LMS) and Its Family
- Least Squares Method
- Kalman Filters
- Blind Deconvolution/Equalization
- Paulo Sérgio Ramirez Diniz, "Adaptive Filtering: Algorithms and Practical Implementation". Springer, 4th edition, 2013
- Simon Haykin, "Adaptive Filter Theory". Prentice Hall, 5th edition, 2013
- Ali H. Sayed, "Adaptive Filters". Wiley-IEEE Press, 2008.
Material (in Portuguese)