Syllabus

Module 1: Introduction to Adaptive Signal Processing [7 hr.]
Stationary process and models, Statistical estimation of signals, Power Spectrum analysis and estimation, Eigen analysis

Module 2: Linear Optimal Filtering [7 hr.]
Wiener Filter, Linear prediction, Kalman filtering

Module 3: Linear Adaptive Filtering [8 hr.]
Steepest Descent method, Least Mean Square (LMS) algorithm, Frequency domain adaptive filter, Rotation & Reflection Operator, Recursive Least-square algorithm, Square root adaptive filter

Module 4: Non-linear Adaptive Filters & Dimension Reduction [7 hr.]
Blind deconvolution & Independent Component Analysis, Principal Component Analysis (PCA)

Module-5: Advanced Topics in Adaptive Filtering [6 hr]
Particle Filtering: Methods & Properties

The student will be aware and able to visualize the domain of adaptive signal processing

The student will be able to identify a random process and formulate to extract desired information

The student will be able to develop algorithms meeting application specific performance criteria

The student will be able to implement the adaptive algorithms in software/Hardware

The student will be aware and able to
CO1 Explain scope of the signal processing domain with abstract framework
CO2 Analyse and formulate application problems mathematically
CO3 Develop algorithms meeting application specific performance criteria
CO4 Implement and test performance of the signal processing system in software
CO5: Use modern tools for design, simulation, & realization of systems like Python/MATLAB/SCILAB

D. G. Manolakis, V.K. Ingle, S.M. Kogon, Adaptive Signal Processing, McGraw-Hill, , 2000.

S. Haykin and T. Kailath, Adaptive Filter Theory, Pearson Education , 2005

B. Widrow and S. D. Sterns, Adaptive Signal Processing, Pearson Education , 2002

J. Benesty, Y. Huang, Adaptive Signal processing: Applications to Real World Problems, Springer , 2003