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Detection and estimation for communication and radar systems /
"Covering the fundamentals of detection and estimation theory, this systematic guide describes statistical tools that can be used to analyze, design, implement and optimize real-world systems. Detailed derivations of the various statistical methods are provided, ensuring a deeper understanding...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge :
Cambridge University Press,
2013.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Yao, Kung. | |
| 245 | 1 | 0 | |a Detection and estimation for communication and radar systems / |c Kung Yao, Flavio Lorenzelli, Chiao-En Chen. |
| 260 | |a Cambridge : |b Cambridge University Press, |c 2013. | ||
| 300 | |a 1 online resource (x, 322 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a data file |2 rda | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "Covering the fundamentals of detection and estimation theory, this systematic guide describes statistical tools that can be used to analyze, design, implement and optimize real-world systems. Detailed derivations of the various statistical methods are provided, ensuring a deeper understanding of the basics. Packed with practical insights, it uses extensive examples from communication, telecommunication and radar engineering to illustrate how theoretical results are derived and applied in practice. A unique blend of theory and applications and over 80 analytical and computational end-of-chapter problems make this an ideal resource for both graduate students and professional engineers"-- |c Provided by publisher. | ||
| 505 | 0 | |a Preface; 1 Introduction and motivation to detection and estimation; 1.1 Introduction; 1.2 A simple binary decision problem; 1.3 A simple correlation receiver; 1.4 Importance of SNR and geometry of the signal vectors in detection theory; 1.5 BPSK communication systems for different ranges; 1.6 Estimation problems; 1.6.1 Two simple estimation problems; 1.6.2 Least-absolute-error criterion; 1.6.3 Least-square-error criterion; 1.6.4 Estimation robustness; 1.6.5 Minimum mean-square-error criterion; 1.7 Conclusions; 1.8 Comments; References; Problems. | |
| 505 | 8 | |a 2 Review of probability and random processes2.1 Review of probability; 2.2 Gaussian random vectors; 2.2.1 Marginal and conditional pdfs of Gaussian random vectors; 2.3 Random processes (stochastic processes); 2.4 Stationarity; 2.5 Gaussian random process; 2.6 Ensemble averaging, time averaging, and ergodicity; 2.7 WSS random sequence; 2.8 Conclusions; 2.9 Comments; 2.A Proof of Theorem 2.1 in Section 2.2.1; 2.B Proof of Theorem 2.2 in Section 2.2.1; References; Problems; 3 Hypothesis testing; 3.1 Simple hypothesis testing; 3.2 Bayes criterion; 3.3 Maximum a posteriori probability criterion. | |
| 505 | 8 | |a 3.4 Minimax criterion3.5 Neyman -- Pearson criterion; 3.6 Simple hypothesis test for vector measurements; 3.7 Additional topics in hypothesis testing (*); 3.7.1 Sequential likelihood ratio test (SLRT); 3.7.2 Uniformly most powerful test; 3.7.3 Non-parametric sign test; 3.8 Conclusions; 3.9 Comments; References; Problems; 4 Detection of known binary deterministic signals in Gaussian noises; 4.1 Detection of known binary signal vectors in WGN; 4.2 Detection of known binary signal waveforms in WGN; 4.3 Detection of known deterministic binary signal vectors in colored Gaussian noise. | |
| 505 | 8 | |a 4.4 Whitening filter interpretation of the CGN detector4.5 Complete orthonormal series expansion; 4.6 Karhunen -- Loeve expansion for random processes; 4.7 Detection of binary known signal waveforms in CGN via the KL expansion method; 4.8 Applying the WGN detection method on CGN channel received data (*); 4.8.1 Optimization for evaluating the worst loss of performance; 4.9 Interpretation of a correlation receiver as a matched filter receiver; 4.10 Conclusions; 4.11 Comments; 4.A; 4.B; References; Problems; 5 M-ary detection and classification of deterministic signals; 5.1 Introduction. | |
| 505 | 8 | |a 5.2 Gram -- Schmidt orthonormalization method and orthonormal expansion5.3 M-ary detection; 5.4 Optimal signal design for M-ary systems; 5.5 Classification of M patterns; 5.5.1 Introduction to pattern recognition and classification; 5.5.2 Deterministic pattern recognition; 5.6 Conclusions; 5.7 Comments; References; Problems; 6 Non-coherent detection in communication and radar systems; 6.1 Binary detection of a sinusoid with a random phase; 6.2 Performance analysis of the binary non-coherent detection system; 6.3 Non-coherent detection in radar receivers; 6.3.1 Coherent integration in radar. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Signal detection. | |
| 650 | 6 | |a Traitement du signal. | |
| 650 | 6 | |a Détection du signal. | |
| 650 | 7 | |a COMPUTERS |x Information Theory. |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Signals & Signal Processing. |2 bisacsh | |
| 650 | 7 | |a Signal detection |2 fast | |
| 650 | 7 | |a Signal processing |2 fast | |
| 700 | 1 | |a Lorenzelli, Flavio. | |
| 700 | 1 | |a Chen, Chiao-En. | |
| 758 | |i has work: |a Detection and estimation for communication and radar systems (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFCgDQkbMVVpF9c88Wxwhb |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Yao, Kung. |t Detection and estimation for communication and radar systems. |d Cambridge : Cambridge University Press, 2013 |w (DLC) 2012037577 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1099807 |z Texto completo |
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