Principles of signal detection and parameter estimation
by
Levy, Bernard C
.
Publisher: New York: Springer, 2008Description: xviii, 644p.ISBN: 0387765425; 9780387765426.Subject(s): Parameter estimation | Signal detectionSummary: This new textbook is for contemporary signal detection and parameter estimation courses offered at the advanced undergraduate and graduate levels. It presents a unified treatment of detection problems arising in radar/sonar signal processing and modern digital communication systems. The material is comprehensive in scope and addresses signal processing and communication applications with an emphasis on fundamental principles. In addition to standard topics normally covered in such a course, the author incorporates recent advances, such as the asymptotic performance of detectors, sequential detection, generalized likelihood ratio tests (GLRTs), robust detection, the detection of Gaussian signals in noise, the expectation maximization algorithm, and the detection of Markov chain signals. Numerous examples and detailed derivations along with homework problems following each chapter are included.
| Item type | Location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
| Books |
Central Library AIOU Islamabad
General Stacks
Allama Iqbal Open UniversityCentral Library |
621.3822 LEP (Browse shelf) | Available | 112730 |
HB
This new textbook is for contemporary signal detection and parameter estimation courses offered at the advanced undergraduate and graduate levels. It presents a unified treatment of detection problems arising in radar/sonar signal processing and modern digital communication systems. The material is comprehensive in scope and addresses signal processing and communication applications with an emphasis on fundamental principles. In addition to standard topics normally covered in such a course, the author incorporates recent advances, such as the asymptotic performance of detectors, sequential detection, generalized likelihood ratio tests (GLRTs), robust detection, the detection of Gaussian signals in noise, the expectation maximization algorithm, and the detection of Markov chain signals. Numerous examples and detailed derivations along with homework problems following each chapter are included.
Eng
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