Adaptive Signal Processing NPTEL
Online Free Online Course by
World Mentoring Academy
Online
/ Free Online Course
Details
This course will develop two main classes of adaptive filter algorithms, namely the LMS and the RLS algorithms. Towards this, it will develop all necessary mathematical tools, in particular, random variables, stochastic processes and correlation structure.
The filtering problem is developed in the form of computing orthogonal projection on a signal subspace. Adaptive signal processing concerns with processing of signals where the processing parameters are adjusted continuously to suit time varying signal environmental
conditions. Central to adaptive signal processing is the concept of adaptive linear combiner, often called adaptive filter where the combiner (filter) coefficients are trained continuously so that the filter can estimate and track an unknown, target signal.
Resources: OpenCourseware from NPTEL (India), Sheridan College, MIT, UC Berkeley, Stanford & many other of the World's finest University's.
Language: English
Units: 41
Lesson content
- Lec 1 Introduction
- Lec 2 Introduction
- Lec 3 Stochastic Processes
- Lec 4 Correlation Structure
- Lec 5 FIR Wiener Filter (Real)
- Lec 6 Steepest Descent Technique
- Lec 7 LMS Algorithm
- Lec 8 Convergence Analysis
- Lec 9 Convergence Analysis (Mean Square)
- Lec 10 Convergence Analysis (Mean Square)
- Lec 11 Misadjustment and Excess MSE
- Lec 12 Misadjustment and Excess MSE
- Lec 13 Sign LMS Algorithm
- Lec 14 Block LMS Algorithm
- Lec 15 Fast Implementation of Block LMS Algorithm
- Lec 16 Fast Implementation of Block LMS Algorithm
- Lec 17 Vector Space Treatment to Random Variables
- Lec 18 Vector Space Treatment to Random Variables
- Lec 19 Orthogonalization and Orthogonal Projection
- Lec 20 Orthogonal Decomposition of Signal Subspaces
- Lec 21 Introduction to Linear Prediction
- Lec 22 Lattice Filter
- Lec 23 Lattice Recursions
- Lec 24 Lattice as Optimal Filter
- Lec 25 Linear Prediction and Autoregressive Modeling
- Lec 26 Gradient Adaptive Lattice
- Lec 27 Gradient Adaptive Lattice
- Lec 28 Introduction to Recursive Least Squares
- Lec 29 RLS Approach to Adaptive Filters
- Lec 30 RLS Adaptive Lattice
- Lec 31 RLS Lattice Recursions
- Lec 32 RLS Lattice Recursions
- Lec 33 RLS Lattice Algorithm
- Lec 34 RLS Using QR Decomposition
- Lec 35 Givens Rotation
- Lec 36 Givens Rotation and QR Decomposition
- Lec 37 Systolic Implementation
- Lec 38 Systolic Implementation
- Lec 39 Singular Value Decomposition
- Lec 40 Singular Value Decomposition
- Lec 41 Singular Value Decomposition
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