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

  1. The student will be aware and able to visualize the domain of adaptive signal processing
  2. The student will be able to identify a random process and formulate to extract desired information
  3. The student will be able to develop algorithms meeting application specific performance criteria
  4. 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
• 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

Essential Reading

  1. D. G. Manolakis, V.K. Ingle, S.M. Kogon, Adaptive Signal Processing, McGraw-Hill, , 2000.
  2. S. Haykin and T. Kailath, Adaptive Filter Theory, Pearson Education , 2005

Supplementary Reading

  1. B. Widrow and S. D. Sterns, Adaptive Signal Processing, Pearson Education , 2002
  2. J. Benesty, Y. Huang, Adaptive Signal processing: Applications to Real World Problems, Springer , 2003