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Adaptive Signal Processing

Adaptive Signal Processing. Instructor: Prof. Mrityunjoy Chakraborty, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. This course covers lessons on Adaptive Filters, Stochastic Processes, Correlation Structure, Convergence Analysis, LMS Algorithm, Vector Space Treatment to Random Variables, Gradient Adaptive Lattice, Recursive Least Squares, Systolic Implementation and Singular Value Decomposition. (from nptel.ac.in)

Introduction to Adaptive Filters


Lecture 01 - Introduction to Adaptive Filters
Lecture 02 - Introduction to Stochastic Processes
Lecture 03 - Stochastic Processes (cont.)
Lecture 04 - Correlation Structure
Lecture 05 - FIR Wiener Filter (Real)
Lecture 06 - Steepest Descent Technique
Lecture 07 - LMS Algorithm
Lecture 08 - Convergence Analysis (in Mean)
Lecture 09 - Convergence Analysis (Mean Square)
Lecture 10 - Convergence Analysis (Mean Square) (cont.)
Lecture 11 - Misadjustment and Excess MSE
Lecture 12 - Misadjustment and Excess MSE (cont.)
Lecture 13 - Sign LMS Algorithm
Lecture 14 - Block LMS Algorithm
Lecture 15 - Fast Implementation of Block LMS Algorithm
Lecture 16 - Fast Implementation of Block LMS Algorithm (cont.)
Lecture 17 - Vector Space Treatment to Random Variables
Lecture 18 - Vector Space Treatment to Random Variables (cont.)
Lecture 19 - Orthogonalization and Orthogonal Projection
Lecture 20 - Orthogonal Decomposition of Signal Subspaces
Lecture 21 - Introduction to Linear Prediction
Lecture 22 - Lattice Filter
Lecture 23 - Lattice Recursions
Lecture 24 - Lattice as Optimal Filter
Lecture 25 - Linear Prediction and Autoregressive Modeling
Lecture 26 - Gradient Adaptive Lattice
Lecture 27 - Gradient Adaptive Lattice (cont.)
Lecture 28 - Introduction to Recursive Least Squares (RLS)
Lecture 29 - RLS Approach to Adaptive Filters
Lecture 30 - RLS Adaptive Lattice
Lecture 31 - RLS Lattice Recursions
Lecture 32 - RLS Lattice Recursions (cont.)
Lecture 33 - RLS Lattice Algorithm
Lecture 34 - RLS using QR Decomposition
Lecture 35 - Givens Rotation
Lecture 36 - Givens Rotation and QR Decomposition
Lecture 37 - Systolic Implementation
Lecture 38 - Systolic Implementation (cont.)
Lecture 39 - Singular Value Implementation
Lecture 40 - Singular Value Implementation (cont.)
Lecture 41 - Singular Value Implementation (cont.)

References
Adaptive Signal Processing
Instructor: Prof. Mrityunjoy Chakraborty, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. This course covers lessons on adaptive signal processing.