Fundamentals of Statistical Processing, Volume I:Estimation TheoryDescription |
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.  |
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Features |
- describes the field of parameter estimation based on time series data.
- provides a summary of principal approaches as well as a “roadmap” to use in the selection of an estimator.
- extends many of the results for real data/real parameters to complex data/complex parameters.
- summarizes as examples many of the important estimators used in practice.
- illustrates how a digital computer can be used to assess performance of an estimator.
- emphasizes a linear model to allow an optimal estimator to be found by inspection of a data model.
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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.
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