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Principles of Signal Detection and Parameter Estimation

Autor Bernard C. Levy
en Limba Engleză Paperback – 5 noi 2010
Asadiscipline,signaldetectionhasevolvedsigni?cantlyoverthelast40years. Some changes have been caused by technical advances, like the development of robust detection methods, or the use of the theory of large deviations to characterize the asymptotic performance of tests, but most changes have been caused by transformations in the engineering systems to which detection te- niques are applied. While early applications of signal detection focused on radar and sonar signal processing or the design of digital communication - ceivers, newer areas of application include image analysis and interpretation, document authenti?cation, biometrics, and sensor or actuator failure det- tion. This expanded scope of application has required some adjustment in standardwaysofformulatingdetectionproblems. Forexample,imageproce- ing applications typically combine parameter estimation and detection tasks, so the separation of parameter estimation and detection in distinct operations typical of early communication systems, where parameter estimation was - complished through the use of training signals, needs to be abandoned. Other changes have occured in the design of communication systems which make it increasingly di?cult to treat the detection of communications signals and of radar/sonar signals in a uni?ed manner. This common framework assumes implicitly that intersymbol interference is not present and that channel c- ing and modulation are implemented separately, since in this case modulated signals can be detected one symbol at a time. But modern communication systems are typically designed to operate over bandlimited channels where - tersymbol interference is present, and starting with the introduction of trellis coded modulation, modulation and coding have become intertwined.
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Specificații

ISBN-13: 9781441945655
ISBN-10: 1441945652
Pagini: 660
Ilustrații: 664 p. 101 illus.
Dimensiuni: 155 x 235 x 35 mm
Greutate: 0.91 kg
Ediția:Softcover reprint of hardcover 1st ed. 2008
Editura: Springer Us
Colecția Springer
Locul publicării:New York, NY, United States

Public țintă

Graduate

Cuprins

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.

Textul de pe ultima copertă

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.

Caracteristici

For graduate EE courses on Detection and Estimation Theory Modern classical communications theory approach Includes both analog and digital communication New developments such as detection of Markov Chains, Gaussian and Robust detection, sequential testing, etc. Problems, Solutions Manual Includes supplementary material: sn.pub/extras Request lecturer material: sn.pub/lecturer-material