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1 |
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|a Yao, Kung.
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|a Detection and estimation for communication and radar systems /
|c Kung Yao, Flavio Lorenzelli, Chiao-En Chen.
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| 260 |
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|a Cambridge :
|b Cambridge University Press,
|c 2013.
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| 300 |
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|a 1 online resource (x, 322 pages) :
|b illustrations
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a data file
|2 rda
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|a Includes bibliographical references and index.
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|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.
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|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.
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|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.
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|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.
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|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.
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|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.
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| 650 |
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|a Signal processing.
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| 650 |
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|a Signal detection.
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| 650 |
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|a COMPUTERS
|x Information Theory.
|2 bisacsh
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|a TECHNOLOGY & ENGINEERING
|x Signals & Signal Processing.
|2 bisacsh
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| 650 |
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|a Signal detection
|2 fast
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| 650 |
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|a Signal processing
|2 fast
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| 700 |
1 |
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|a Lorenzelli, Flavio.
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| 700 |
1 |
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|a Chen, Chiao-En.
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| 758 |
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|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
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| 776 |
0 |
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|i Print version:
|a Yao, Kung.
|t Detection and estimation for communication and radar systems.
|d Cambridge : Cambridge University Press, 2013
|w (DLC) 2012037577
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| 856 |
4 |
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|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=1099807
|y Click for online access
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