Adaptive Signal Processing
To introduce some practical aspects of signal processing, and in particular adaptive systems. Current applications for adaptive systems are in the fields of communications, radar, sonar, seismology, navigation systems and biomedical engineering. This course will present the basic principles of adaptation, will cover various adaptive signal processing algorithms (e.g., the LMS algorithm) and many applications, such as adaptive noise cancellation, interference canceling, system identification, etc.
B. Widrow and S. Stearns (1985). Adaptive Signal Processing, Prentice Hall.
S. Haykin (1996). Adaptive Filter Theory, (3rd Edition), Prentice Hall.
- Introduction to discrete-time signal processing (Chap. 7)
- Impulse response, z-transform, FIR, IIR filters
- Correlation functions and power spectral density
- The adaptive linear combiner (Chap. 2)
- Introduction to gradient search algorithms, steepest-descent algorithm, convergence properties, Newton algorithm. (Chap. 3, 4, 5)
- Adaptive algorithms- LMS algorithm, Recursive Least Squares algorithm, LMS/Newton algorithm (Chap. 6, 8)
- Frequency domain adaptive filters
- Applications of adaptive signal processing (Chap. 9)
- adaptive modeling and system identification
- inverse adaptive modeling, deconvolution and equalization
- adaptive control systems
- adaptive interference canceling
canceling noise, canceling periodic interference, canceling interference in ECG signals, etc.
- Linear optimum filtering (if time permits)
- Wiener filters
- Kalman filters
To download the syllabus (in pdf format) click here.