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Estimation of Signals and Systems
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Estimation of Signals and Systems
by Prof S Mukhopadhyay

This course covers lessons on probability theory,random variables,mean and variance,linear signal models,Z-transform,kalman filter,variants of LSE and estimation problems in instrumentation and control....

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About This Course

This course covers lessons on probability theory,random variables,mean and variance,linear signal models,Z-transform,kalman filter,variants of LSE and estimation problems in instrumentation and control....

Course Curriculum
1 Sections | 30 Lecture | 25h 50min total length
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Estimation of Signals and Systems

Estimation of Signals and Systems

| 25h 50min total length
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    Introduction

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    Probability Theory

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    Random Variables

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    Function of Random Variable Joint Density

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    Mean and Variance

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    Random Vectors Random Processes

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    Random Processes and Linear Systems

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    Some Numerical Problems

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    Miscellaneous Topics on Random Process

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    Linear Signal Models

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    Linear Mean Sq.Error Estimation

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    Auto Correlation and Power Spectrum Estimation

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    Z-Transform Revisited Eigen Vectors/Values

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    The Concept of Innovation

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    Last Squares Estimation Optimal IIR Filters

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    Introduction to Adaptive Filters

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    State Estimation

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    Kalman Filter-Model and Derivation

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    Kalman Filter-Derivation (Contd...)

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    Estimator Properties

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    The Time-Invariant Kalman Filter

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    Kalman Filter-Case Study

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    System identification Introductory Concepts

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    Linear Regression-Recursive Least Squares

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    Variants of LSE

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    Least Square Estimation

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    Model Order Selection Residual Tests

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    Practical Issues in Identification

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    Estimation Problems in Instrumentation and Control

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    Conclusion

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