A unified presentation of parameter estimation for those involved in the design and implementation of statistical
signal processing algorithms.
Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous
examples as well as applications to real- world problems.
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.
Table of Contents
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.
