Principles of Signal Detection and Parameter Estimation
By: Levy, Bernard C [author.].
Contributor(s): SpringerLink (Online service).
Material type:
BookPublisher: Boston, MA : Springer US, 2008.Description: 664 p. 101 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387765440.Subject(s): Engineering | Information theory | Statistics | Electrical engineering | Engineering | Communications Engineering, Networks | Signal, Image and Speech Processing | Information and Communication, Circuits | Statistics for Engineering, Physics, Computer Science, Chemistry and Earth SciencesDDC classification: 621.382 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK539 |
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.
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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