CE836 Adaptive Signal Processing

Course Name: 

CE836 Adaptive Signal Processing


M.Tech (CE)


Elective (Ele)

Credits (L-T-P): 

(3-0-0) 3


Introduction to Adaptive Processing: General properties, filtering, prediction and smoothing, Applications in Communications: Equalisation, Echo cancellation, Noise cancellation. Optimal Signal Processing, Principles of orthogonality, minimum square error, Wiener Hopf equations, state space model, innovations process, Kalman filter equations. Linear Adaptive Equalisation, Gradient search and steepest descent adaptation algorithms, effect of eigenvalue spread on stability and rate of convergence, stochastic gradient descent using Least Mean Squares (LMS) algorithms, transient and steady state properties including convergence rate and mis-adjustment, least square estimation, normal equations, Recursive Least Squares (RLS) algorithms, relationship between RCS and Kalman filters. Introduction to Fast Recursive Algorithms for Equalization, Adaptive linear prediction, lattice filtering for RLS. Tracking time-varying systems, Nonlinear adaptive filtering


S.J. Orfanidis, Optimum Signal Processing, McGraw Hill, 1989.
S. Haykin, Adaptive Filter Theory, Pearson, 1996

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Dr. T. Laxminidhi,  Professor and Head, 
Department of E&C, NITK, Surathkal
P. O. Srinivasnagar,
Mangalore - 575 025 Karnataka, India.

  • Hot line: +91-0824-2473046

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