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 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.
Why Read This Book
You will gain a rigorous, example-driven mastery of detection and estimation theory tailored to real-world radar and communication problems, with full derivations that connect abstract statistics to practical system design. The book balances theory and application so you can both prove performance bounds and implement effective detectors and estimators in practice.
Who Will Benefit
Graduate students, signal-processing engineers, and communication/radar practitioners who need a rigorous, application-oriented reference for statistical detection and estimation in real systems.
Level: Advanced — Prerequisites: Undergraduate calculus and linear algebra, probability and random processes (including PDFs, expectation, and basic stochastic processes), signals and systems fundamentals, and familiarity with MATLAB or similar numerical tools.
Key Takeaways
- Derive and apply Neyman–Pearson, likelihood-ratio, and GLRT decision rules to binary and composite hypothesis tests
- Analyze detector performance using ROC curves, false alarm/ detection trade-offs, and exact/approximate performance expressions
- Formulate and solve parameter estimation problems using unbiased/biased estimators, ML and Bayesian approaches, and compute Cramér–Rao bounds
- Design matched filters and optimal receivers for communication and radar scenarios under noise and interference
- Apply statistical tools to real-world examples in communications and radar, including detector implementation and performance optimization
Topics Covered
- 1. Introduction and Overview of Detection & Estimation
- 2. Statistical Preliminaries: PDFs, Expectations, and Likelihoods
- 3. Fundamentals of Hypothesis Testing and Neyman–Pearson Lemma
- 4. Composite Hypotheses, Generalized Likelihood Ratio Tests, and CFAR Concepts
- 5. Detector Performance: ROC Curves, Operating Points, and Asymptotics
- 6. Matched Filtering and Optimal Receivers in Gaussian Noise
- 7. Parameter Estimation: Point Estimators, Bias, and Consistency
- 8. Maximum Likelihood and Bayesian Estimation Techniques
- 9. Fisher Information and Cramér–Rao Bounds
- 10. Practical Radar and Communication Examples and Case Studies
- 11. Extensions: Adaptive Detection, Multiple-Antenna Scenarios, and Sequential Tests
- 12. Computational Methods and End-of-Chapter Problems
Languages, Platforms & Tools
How It Compares
Closely related to Steven M. Kay's Fundamentals of Statistical Signal Processing but with more emphasis on communication and radar examples; more application-focused than Van Trees' multi-volume Detection, Estimation, and Modulation Theory.
