Contents:
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 Square 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 I – Estimator Properties-The Time-Invariant Kalman Filter – Kalman Filte r- 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

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