Why Read This Book

You will gain a clear, application-focused grounding in classical detection theory and learn how to design and evaluate practical detectors for radar, communications, audio and other noisy-signal environments. The book emphasizes likelihood-ratio and matched-filter approaches, performance measures (ROC, false-alarm/ detection probabilities), and worked examples that connect mathematical theory to real engineering problems.

Who Will Benefit

Signal-processing engineers, radar and communications practitioners, and graduate students who need a rigorous yet application-oriented treatment of statistical detection and practical detector design.

Level: Advanced — Prerequisites: Undergraduate probability and random processes, signals and systems (including Fourier transforms), linear algebra, calculus, and basic digital signal processing concepts (filters, sampling, FFT).

Key Takeaways

• Derive and apply likelihood-ratio and Neyman–Pearson tests for signal detection problems
• Design and implement matched filters and correlator receivers for known-signal detection
• Evaluate detector performance using ROC curves, detection and false-alarm probabilities, and SNR analysis
• Apply spectral-analysis and FFT-based methods to detect signals in noisy environments
• Use adaptive filtering and interference mitigation techniques to improve detection in nonstationary or unknown-noise scenarios

Topics Covered

1. 1. Introduction to Detection Problems and Practical Examples
2. 2. Review of Probability, Random Processes, and Statistical Measures
3. 3. Hypothesis Testing and the Neyman–Pearson Criterion
4. 4. Likelihood-Ratio Tests and Decision Rules
5. 5. Matched Filters, Correlation Receivers, and Optimal Linear Detectors
6. 6. Detection in Gaussian and Non-Gaussian Noise
7. 7. Spectral Methods, FFT, and Frequency-Domain Detectors
8. 8. Adaptive Detection and Adaptive Filtering Techniques
9. 9. Detection in Radar and Sonar Systems
10. 10. Communication Signal Detection and Demodulation Considerations
11. 11. Performance Analysis: ROC, SNR, and Asymptotic Results
12. 12. Practical Implementation Issues and Examples

Languages, Platforms & Tools

How It Compares

More application-oriented and engineering-focused than Steven Kay's Fundamentals of Statistical Signal Processing: Detection Theory, and more accessible for radar/communications practitioners than Van Trees' multi-volume Detection, Estimation, and Modulation Theory.