Syllabus

Module 1: Introduction to Adaptive Signal Processing [6 hr.]
Stationary process and models, Statistical estimation of signals, Power Spectrum analysis and estimation, Eigen analysis

Module 2: Linear Optimal Filtering [8 hr.]
Wiener Filter, Linear prediction, Kalman filtering

Module 3: Linear Adaptive Filtering [10 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 [7 hr.]
Blind deconvolution & Independent Component Analysis, Principal Component Analysis (PCA)

Module-5: Software Simulation and Practical Applications [5 hr]
Object Oriented Programming for Signal Processing, Development of algorithm and coding for simulation of solution of equations, Eigenvalue problems, random process and adaptive filters, Concept of computational complexity

To make the students adept to visualize the domain of adaptive signal processing

To make the students adept to identify a random process and formulate to extract desired information

To make the students adept to develop algorithms meeting application specific performance criteria

To make the students adept to implement the adaptive algorithms in software/Hardware

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: Students will be able to 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 or Latest Ed.

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

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

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