Syllabus
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Course Code: *Elective-V MTEC-201 Course Name: Adaptive Filter Theory |
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| MODULE NO / UNIT | COURSE SYLLABUS CONTENTS OF MODULE | NOTES |
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| 1 | Introduction:-Variance of a random variable, Estimation: Given No Observations, Given Dependent
Observations, Complex and Vector Cases, Normal Equations, Design Examples, Linear Models and
applications. Minimum-Variance Unbiased Estimation and applications. Steepest-Descent Algorithms:- Steepest-Descent Method, Transient Behavior, Iteration-Dependent Step-Sizes, Newton’s Method. |
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| 2 | Stochastic-Gradient Algorithms:- LMS Algorithm and applications, Normalized LMS Algorithm, Non-Blind
Algorithms, Blind Algorithms and properties, Affine Projection Algorithms, Ensemble-Average Learning Curves. Steady-State Performance of Adaptive Filters:- Performance Measures, Stationary Data Model, Fundamental Energy-Conservation Relation, Fundamental Variance Relation, Mean-Square Performance of LMS and εNLMS. |
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| 3 | Tracking Performance of Adaptive Filters:-Non-stationary Data Model, Fundamental Energy-Conservation Relation, Fundamental Variance Relation, Tracking Performance of LMS and ε-NLMS. Transient Performance of Adaptive Filters:-Data Model, Data-Normalized Adaptive Filters, Weighted EnergyConservation Relation, Weighted Variance Relation, Transient Performance of LMS and ε-NLMS. | |
| 4 | Recursive Least-Squares:-RLS Algorithm, Exponentially-Weighted RLS Algorithm, RLS Array Algorithms: Square-Root Factors, Norm and Angle Preservation, Motivation for Array Methods, RLS Algorithm, Inverse QR Algorithm, QR Algorithm, Extended QR Algorithm. | |
