v. 1. Estimation theory v. 2. Detection theory v. 3. Practical algorithm development

Includes index.

1 Introduction 2 Minimum Variance Unbiased Estimation 3 Cramer-Rao Lower Bound 4 Linear Models 5 General Minimum Variance Unbiased Estimation 6 Best Linear Unbiased Estimators 7 Maximum Likelihood Estimation 8 Least Squares 9 Method of Moments 10 The Bayesian Philosophy 11 General Bayesian Estimators 12 Linear Bayesian Estimators 13 Kalman Filters 14 Summary of Estimators 15 Extension for Complex Data and Parameters Appendix: Review of Important Concepts Glossary of Symbols and Abbreviations

For those involved in the design and implementation of signal processing algorithms, this book strikes a balance between highly theoretical expositions and the more practical treatments, covering only those approaches necessary for obtaining an optimal estimator and analyzing its performance. Author Steven M. Kay discusses classical estimation followed by Bayesian estimation, and illustrates the theory with numerous pedagogical and real-world examples.