An Introduction to Signal Detection and Estimation (Springer Texts in Electrical Engineering)
Essential background reading for engineers and scientists working in such fields as communications, control, signal, and image processing, radar and sonar, radio astronomy, seismology, remote sensing, and instrumentation. The book can be used as a textbook for a single course, as well as a combination of an introductory and an advanced course, or even for two separate courses, one in signal detection, the other in estimation.
Why Read This Book
You will gain a rigorous, mathematically grounded foundation in statistical detection and estimation that directly underpins modern DSP, radar, and communications systems. The book emphasizes principle-driven methods (likelihood ratios, Cramér–Rao bounds, ML/MAP estimators, sequential tests) so you can analyze and design reliable detectors and estimators rather than only using black‑box algorithms.
Who Will Benefit
Graduate students and practicing engineers in communications, radar, sonar, audio/speech, and signal processing who need a deep theoretical grounding to design and analyze detectors and estimators.
Level: Advanced — Prerequisites: Solid calculus, linear algebra, probability and random processes (including Gaussian processes), and basic familiarity with signals and systems or introductory DSP.
Key Takeaways
- Derive and apply optimal hypothesis tests (Neyman–Pearson, likelihood‑ratio) for common signal+noise models.
- Compute and use performance metrics (ROC curves, false/true alarm probabilities) to evaluate detectors.
- Formulate and implement classical and Bayesian estimators (MLE, MAP, MMSE) and analyze their properties.
- Derive and apply the Fisher information and Cramér–Rao lower bounds to assess estimator efficiency.
- Design and analyze sequential detection procedures (e.g., SPRT) and composite hypothesis tests (GLRT).
- Apply detection and estimation theory to practical domains such as radar, communications receivers, and spectral analysis.
Topics Covered
- 1. Introduction and Problem Formulation
- 2. Binary Hypothesis Testing and the Neyman–Pearson Lemma
- 3. Detection of Deterministic Signals in Gaussian Noise
- 4. Detection of Random Signals and Gaussian Vector Models
- 5. Composite Hypotheses, Generalized Likelihood Ratio Tests, and Invariance
- 6. Performance Measures: ROC, Error Exponents, and Asymptotic Analysis
- 7. Sequential Detection and the SPRT
- 8. Fundamentals of Estimation: Bayesian and Classical Formulations
- 9. Maximum Likelihood Estimation and Asymptotic Theory
- 10. Fisher Information, Cramér–Rao Bounds, and Efficiency
- 11. Linear Estimation, Least Squares, and MMSE Estimators
- 12. State‑Space Models and Recursive Estimation (Kalman filtering context)
- 13. Practical Issues: Finite‑Sample Behavior, Model Mismatch, and Implementation Notes
- Appendices: Useful Results from Probability and Linear Algebra
How It Compares
Covers similar theoretical ground to Steven Kay's two‑volume Fundamentals of Statistical Signal Processing but is more concise and principle‑focused; Van Trees is broader and more classical in scope, while Poor emphasizes modern statistical perspectives and asymptotic analysis.
