Estimation of Signals and Systems


Lecture 1 - Introduction


Lecture 2 - Probability Theory


Lecture 3 - Random Variables


Lecture 4 - Function of Random Variable Joint Density


Lecture 5 - Mean and Variance


Lecture 6 - Random Vectors Random Processes


Lecture 7 - Random Processes and Linear Systems


Lecture 8 - Some Numerical Problems


Lecture 9 - Miscellaneous Topics on Random Process


Lecture 10 - Linear Signal Models


Lecture 11 - Linear Mean Sq.Error Estimation


Lecture 12 - Auto Correlation and Power Spectrum Estimation


Lecture 13 - Z-Transform Revisited Eigen Vectors/Values


Lecture 14 - The Concept of Innovation


Lecture 15 - Last 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 (Continued...)


Lecture 20 - Estimator Properties


Lecture 21 - The Time-Invariant Kalman Filter


Lecture 22 - Kalman Filter-Case Study


Lecture 23 - System identification Introductory Concepts


Lecture 24 - Linear Regression-Recursive Least Squares


Lecture 25 - Variants of LSE


Lecture 26 - Least Square 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