For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals ― radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.

A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms.

Introduction 
Minimum Variance Unbiased Estimation 
Cramer-Rao Lower Bound 
Linear Models 
General Minimum Variance Unbiased Estimation 
Best Linear Unbiased Estimators 
Maximum Likelihood Estimation 
Least Squares 
Method of Moments 
The Bayesian Philosophy 
General Bayesian Estimators 
Linear Bayesian Estimators 
Kalman Filters 
Summary of Estimators 
Extension for Complex Data and Parameters 
Appendix: Review of Important Concepts 
Glossary of Symbols and Abbreviations