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Course Detail

Course Name Adaptive Signal Processing
Course Code 15ECE321
Program B. Tech. in Electronics and Communication Engineering
Year Taught 2019


Unit 1

Adaptive Systems – Definition and characteristics – Properties – Applications and examples of an adaptive system. Stochastic Processes and Models: Characterization – Mean ergodic theorem – Correlation matrix – Stochastic models – Power spectral density – Properties of power spectral Density – Linear transformations – Power spectral estimation.

Unit 2

Wiener filters – Linear optimum filtering – Minimum mean-square error – Wiener- Hopf equations – Multiple linear regression model – Steepest-descent algorithm – Linear prediction – Forward linear prediction, Levinson-Durbin algorithm. Kalman filter – Extended kalman filter

Unit 3

Least-Mean-Square (LMS) adaptive filters – LMS algorithm, LMS adaptation algorithm – applications. Method of Least Squares – Data windowing, Normal equations and linear least square filters, Recursive least squares algorithm.

Text Books

  1. Simon Haykins, “Adaptive Filter Theory”, Pearson Education, Fifth Edition, 2013.


  1. Todd K. Moon, Wynn C. Stirling, “Mathematical Methods and Algorithms for Signal Processing” Prentice Hall, First edition, 1999.
  2. John. R. Triechler, C. Richard Johnson (Jr), Michael. G. Larimore, “Theory and Design of Adaptive Filters”, Prentice Hall India Private Limited, 2004
  3. Bernard Widrow and Samuel. D. Stearns, “Adaptive Signal Processing”, Pearson Education, 2001.

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