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Estimation of Signals and Systems

Estimation of Signals and Systems. Instructor: Prof. S. Mukhopadhyay, Department of Electrical Engineering, IIT Kharagpur. This course covers lessons on probability theory, random variables, mean and variance, linear signal models, z-transform, Kalman filter, variants of least squares estimation, and estimation problems in instrumentation and control. (from nptel.ac.in)

Lecture 12 - Autocorrelation and Power Spectrum Estimation

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Lecture 01 - Introduction

Lecture 02 - Probability Theory

Lecture 03 - Random Variables

Lecture 04 - Function of Random Variable Joint Density

Lecture 05 - Mean and Variance

Lecture 06 - Random Vectors, Random Processes

Lecture 07 - Random Processes and Linear Systems

Lecture 08 - Some Numerical Problems

Lecture 09 - Miscellaneous Topics on Random Process

Lecture 10 - Linear Signal Models

Lecture 11 - Linear Mean Square Error Estimation

Lecture 12 - Autocorrelation and Power Spectrum Estimation

Lecture 13 - z-Transform Revisited, Eigenvectors and Eigenvalues

Lecture 14 - The Concept of Innovation, Linear Minimum Mean Square Estimation

Lecture 15 - Least Squares Estimation, Optimal IIR Filters

Lecture 16 - Introduction to Adaptive Filters

Lecture 17 - State Estimation

Lecture 18 - Kalman Filter Model and Derivation

Lecture 19 - Kalman Filter Derivation (cont.)

Lecture 20 - Estimator Properties - Algebraic and Probabilistic

Lecture 21 - The Time Invariant Kalman Filter, Kalman Filter Coloured Noise

Lecture 22 - Kalman Filter - Case Study

Lecture 23 - System Identification: Introductory Concepts

Lecture 24 - Linear Regression Recursive Least Squares

Lecture 25 - Variants of Least Squares Estimation

Lecture 26 - Least Squares Estimation

Lecture 27 - Model Order Selection Residual Tests

Lecture 28 - Practical Issues in Identification

Lecture 29 - Estimation Problems in Instrumentation and Control

Lecture 30 - Conclusion