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## Book Description

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.

1. Cover
2. Half title
3. Epigraph
4. Title
6. Dedication
8. Preface
9. Chapter 1: Introduction and motivation to detection and estimation
1. 1.1 Introduction
2. 1.2 A simple binary decision problem
3. 1.3 A simple correlation receiver
4. 1.4 Importance of SNR and geometry of the signal vectors in detection theory
5. 1.5 BPSK communication systems for different ranges
6. 1.6 Estimation problems
7. 1.7 Conclusions
9. References
10. Problems
10. Chapter 2: Review of probability and random processes
11. Chapter 3: Hypothesis testing
1. 3.1 Simple hypothesis testing
2. 3.2 Bayes criterion
3. 3.3 Maximum a posteriori probability criterion
4. 3.4 Minimax criterion
5. 3.5 Neyman-Pearson criterion
6. 3.6 Simple hypothesis test for vector measurements
7. 3.7 Additional topics in hypothesis testing (*)
8. 3.8 Conclusions
10. References
11. Problems
12. Chapter 4: Detection of known binary deterministic signals in Gaussian noises
13. Chapter 5: M-ary detection and classification of deterministic signals
1. 5.1 Introduction
2. 5.2 Gram-Schmidt orthonormalization method and orthonormal expansion
3. 5.3 M-ary detection
4. 5.4 Optimal signal design for M-ary systems
5. 5.5 Classification of M patterns
6. 5.6 Conclusions
8. References
9. Problems
14. Chapter 6: Non-coherent detection in communication and radar systems
1. 6.1 Binary detection of a sinusoid with a random phase
2. 6.2 Performance analysis of the binary non-coherent detection system
4. 6.4 Conclusions
6. References
7. Problems
15. Chapter 7: Parameter estimation
1. 7.1 Introduction
2. 7.2 Mean-square estimation
3. 7.3 Linear LS and LAE estimation and related robustness and sparse solutions
4. 7.4 Basic properties of statistical parameter estimation
5. 7.5 Conclusions
7. 7.A Proof of Theorem 7.3.3.1 of Section 7.3.3
8. 7.B Proof of Theorem 7.4.1.1 of Section 7.4.1
9. References
10. Problems
16. Chapter 8: Analytical and simulation methods for system performance analysis
1. 8.1 Analysis of receiver performance with Gaussian noise
2. 8.2 Analysis of receiver performance with Gaussian noise and other random interferences
3. 8.3 Analysis of receiver performance with non-Gaussian noises
4. 8.4 Monte Carlo simulation and importance sampling in communication/radar performance analysis
5. 8.5 Conclusions