Cargando…
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.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
- 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.