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.
- Introduction
- Probability Theory
- Random Variables
- Function of Random Variable Joint Density
- Mean and Variance
- Random Vectors Random Processes
- Random Processes and Linear Systems
- Some Numerical Problems
- Miscellaneous Topics on Random Process
- Linear Signal Models
- Linear Mean Sq.Error Estimation
- Auto Correlation and Power Spectrum Estimation
- Z-Transform Revisited Eigen Vectors/Values
- The Concept of Innovation
- Last Squares Estimation Optimal IIR Filters
- Introduction to Adaptive Filters
- State Estimation
- Kalman Filter-Model and Derivation
- Kalman Filter-Derivation (Contd...)
- Estimator Properties
- The Time-Invariant Kalman Filter
- Kalman Filter-Case Study
- System identification Introductory Concepts
- Linear Regression-Recursive Least Squares
- Variants of LSE
- Least Square Estimation
- Model Order Selection Residual Tests
- Practical Issues in Identification
- Estimation Problems in Instrumentation and Control
- Conclusion
