Course Details
Subject {L-T-P / C} : EE6137 : Statistical Signal Processing { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Dr. Supratim Gupta
Syllabus
Introduction to adaptive signal processing: Stationary process and models, Statistical estimation of signals, Power Spectrum analysis and estimation, Eigen analysis Linear Optimal Filtering: Wiener Filter, Linear prediction, Kalman filtering Linear Adaptive Filtering: Steepest Descent method, Least Mean Square (LMS) algorithm, Frequency domain adaptive filter, Rotation & Reflection Operator, Recursive Least-square algorithm, Square root adaptive filter, Finite precision effect Non-linear adaptive filter: Blind deconvolution & Independent Component Analysis Dimension reduction: Principal Component Analysis Particle Filtering: Method & Properties.
Course Objectives
- 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
Course Outcomes
• The student will be aware and able to visualize the domain of adaptive signal processing <br />• The student will be able to identify a random process and formulate to extract desired information <br />• The student will be able to develop algorithms meeting application specific performance criteria <br />• The student will be able to implement the adaptive algorithms in software/Hardware
Essential Reading
- 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
Supplementary Reading
- 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