Course10810519141 lectures
Introduction to Adaptive Signal Processing
Prof. Mrityunjoy Chakraborty · IIT Kharagpur
About
Adaptive filters play a very significant role in most of today’s signal processing, communication and control applications. Their ability to process data under conditions not predictable a priori make them very useful in a diverse array of applications like radar, sonar, wireless communications, speech and audio processing, instrumentation, exploration geophysics etc. The topic of adaptive filters is very closely related to neural networks and machine learning. While a full course on adaptive filters is very involved and needs concepts from linear algebra, probability and random variables, for this particular treatment, care has been taken to maintain a minimum prerequisite requirement. Mathematical concepts necessary will be developed as part of the course. The main purpose will be to provide the participants an introduction to the subject and to prepare them for taking further studies in this and related subjects.
Intended audience: Students belonging to the following disciplines : Electrical Engg., Electronics and Communication Engg., Instrumentation Engg., Information technology and Computer Science, Geophysics, Statistics
Prerequisites: Basics of Signals and Systems and / or Digital Signal Processing
Lectures
Week 1
- Lecture 1 : Introduction to Adaptive Filters
- Lecture 2 : Probability and Random Variables
- Lecture 3 : General Set of Random Variables
- Lecture 4 : Statistical Impedance, Covariance Matrices
- Lecture 5 : Multivariate Gaussian Density
Week 2
- Lecture -6 : Complex Random Variables
- Lecture- 7 : Introduction to Hermitian Matrices
- Lecture - 8 : Eigenvalues and eigenvectors of Hermitian Matrices
- Lecture -9 : Spectral Decomposition of Hermitian Matrices
- Lecture -10 : Positive Definite and Semidefinite Matrices
Week 3
- Lecture -11 : Introduction to Discrete Time Random Processes
- Lecture -12 : Power Spectral Density (PSD)
- Lecture -13 : PSD and Linear Time Invariant Systems
- Lecture -14: Optimal FIR Filter
- Lecture-15 : Optimal FIR Filter (Contd.)
Week 4
- Lecture -16 : LMS Algorithm
- Lecture -17: Convergence Proof of LMS Algorithm
- Lecture - 18: Convergence Proof of LMS Algorithm (Contd.)
- Lecture -19: Application of Adaptive Filter
- Lecture -20 : Application of Adaptive Filter (Contd.)
Week 5
- Lecture -21 : Application of Adaptive Filter (Contd.)
- Lecture - 22: Applications of Adaptive Filter
- Lecture -23 : Applications of Adaptive Filter
- Lecture -24: Second Order Analysis of LMS Algorithm
- Lecture -25: Second Order Analysis of LMS Algorithm (Contd.)
Week 6
- Lecture -26: Second Order Analysis of LMS Algorithm (Contd.)
- Lecture -27: Second Order Analysis of LMS Algorithm (Contd.)
- Lecture -28: NLMS Algorithm
- Lecture -29: NLMS Algorithm
- Lecture -30: Affine Projection Algorithm (APA)
Week 7
- Lecture -31: Affine Projection Algorithm (APA)
- Lecture -32: Introduction to RLS Algorithm
- Lecture -33: Introduction to RLS Algorithm (Contd.)
- Lecture -34 : Introduction to RLS Algorithm (Contd.)
- Lecture -35: Formulation of the RLS Algorithm
Week 8
- Lecture-36 : Introduction to RLS Algorithm
- Lecture-37 : Derivation of the RLS Transversal Adaptive Filter (Confined)
- Lecture-38 : Formulation of the RLS Algorithm
- Lecture-39 : Derivation of the RLS transversal adaptive filter
- Lecture-40 : Derivation of the RLS transversal adaptive filter
- Lecture-41 : Derivation of the RLS transversal adaptive filter
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Kharagpur
- Department
- Electrical Engineering
- Teacher
- Prof. Mrityunjoy Chakraborty
- Level
- Undergraduate
- Language
- English
- Subjects
- Engineering
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
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