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


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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