Principles of Signal Detection and Parameter Estimation

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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Levy, Bernard C. (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Boston, MA : Springer US, 2008.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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505 0 |a I Foundations -- Binary and Mary Hypothesis Testing -- Tests with Repeated Observations -- Parameter Estimation Theory -- Composite Hypothesis Testing -- Robust Detection -- II Gaussian Detection -- Karhunen Loeve Expansion of Gaussian Processes -- Detection of Known Signals in Gaussian Noise -- Detection of Signals with Unknown Parameters -- Detection of Gaussian Signals in WGN -- EM Estimation and Detection of Gaussian Signals with unknown parameters -- III Markov Chain Detection -- Detection of Markov Chains with Known Parameters -- Detection of Markov Chains with Unknown Parameters. 
520 |a 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. 
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650 0 |a Information theory. 
650 0 |a Statistics. 
650 0 |a Electrical engineering. 
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650 2 4 |a Signal, Image and Speech Processing. 
650 2 4 |a Information and Communication, Circuits. 
650 2 4 |a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 
710 2 |a SpringerLink (Online service) 
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776 0 8 |i Printed edition:  |z 9780387765426 
856 4 0 |u http://dx.doi.org/10.1007/978-0-387-76544-0  |z Full Text via HEAL-Link 
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950 |a Engineering (Springer-11647)