Introduction to Statistical Signal Processing with ApplicationsDescription |
Appropriate for introductory graduate-level courses in Statistical Signal Processing and Detection and Estimation Theory. In An Introduction to Statistical Signal Processing with Applications, these three experienced author/educators cover basic techniques in the processing of stochastic signals and illustrate their use in a variety of specific applications. The text presents both detection and estimation in a clear, concise fashion and reflects recent developments and shifting emphases in the field.  |
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Features |
- draws from a variety of specific applications, including...
- provides thorough coverage of parameter and waveform estimation-including Wiener, Kalman filters-with a balance of theory and application.
- contains separate sections on Gauss-Markov model and likelihood and sufficiency, providing a necessary foundation for understanding detection/estimation theory.
- includes UMP tests and continuous waveform detection in coverage of detection theory, offering both theoretical background and specific applications.
- provides introductory material on nonparametric and robust detection to help illuminate theoretical concepts.
- offers a wealth of worked examples and problems to reinforce readers' understanding of concepts and to present results or applications that reach beyond those mentioned in the text.
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Table of Contents |
1. Introduction.
2. Signals and Systems.
3. Detection Theory.
4. Detection of Signals in Noise.
5. Estimation Theory.
6. Estimation of Waveforms.
7. Further Topics in Detection and Estimation.
8. Applications to Communication and Radar Systems.
9. Miscellaneous Applications.
Appendix A: Bilateral Transforms.
Appendix B: Calculus of Extrema.
Appendix C: Vectors and Matrices.
Subject Index.
Author Index.
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