Subject Code: EC6L023 Subject Name: Adaptive Signal Processing L-T-P: 3-0-0 Credits: 3
Pre-requisite(s): Digital Signal Processing
Introduction to adaptive filters, optimal estimation, linear estimation: normal equation, orthogonality principle, linear models. Constrained linear estimation: minimum variance unbiased estimation, steepest descent algorithms, stochastic gradient algorithms: LMS algorithm, normalized LMS algorithm, RLS algorithm. Steady-state performance of adaptive filters, transient performance of adaptive filters, block adaptive filters, the least-squares criterion, recursive least-squares, lattice filters
Texts/Reference Books:
  1. A. H. Sayed, “Fundamentals of Adaptive Filtering,” Wiley, 2003.
  2. S. Haykin, “Adaptive filter theory,” Fourth edition, Pearson, 2012.
  3. Widrow and Stearns, “Adaptive Signal Processing,” Pearson, 2007.