Why Read This Book

You will learn the theory and practical techniques that push radar beyond classical resolution limits, with a deep treatment of parametric and statistical high-resolution methods. The book connects rigorous performance analysis (CRBs, likelihood methods) to implementable algorithms (MUSIC, Capon, ML) and real radar processing problems so you can design and evaluate advanced radar processors.

Who Will Benefit

Practicing radar engineers, signal-processing researchers, and graduate students who want to design or analyze superresolution radar and array-processing algorithms for range, Doppler, and angular estimation.

Level: Advanced — Prerequisites: Solid undergraduate/graduate background in signals and systems, Fourier transforms, linear algebra, probability and estimation theory, and basic radar principles (pulse/Doppler, range resolution).

Key Takeaways

• Apply parametric spectral estimation methods (e.g., MUSIC, Capon) to resolve closely spaced targets in range, Doppler, and angle.
• Derive and use maximum-likelihood and subspace estimators to obtain high-resolution parameter estimates and assess their biases and variances.
• Quantify resolution limits using Cramér–Rao bounds and understand how SNR, waveform, and array geometry affect performance.
• Design and analyze high-resolution processing chains for radar applications (range-Doppler processing, clutter suppression, and target separation).
• Implement practical algorithms for spectral analysis and superresolution, and evaluate trade-offs between robustness, complexity, and resolution.
• Evaluate the impact of waveform choice, aperture/array configuration, and finite sample effects on achievable resolution.

Topics Covered

1. Introduction and historical perspective on high-resolution radar
2. Fundamentals: signal models for pulsed and continuous-wave radar
3. Resolution: definitions, classical limits, and metrics
4. Classical spectral estimation methods and FFT-based analysis
5. Parametric spectral estimation: autoregressive models and Prony-type methods
6. Subspace methods: MUSIC, Root-MUSIC, and ESPRIT fundamentals
7. Capon (minimum variance) and adaptive beamforming techniques
8. Maximum likelihood estimation and statistical performance analysis
9. Range-Doppler high-resolution processing and moving-target considerations
10. Array processing and high-resolution direction finding
11. Practical issues: finite sample effects, noise, clutter, calibration
12. Waveform considerations, system design trade-offs, and application examples
13. Appendices: mathematical tools, derivations, and implementation notes

Languages, Platforms & Tools

How It Compares

Compared with Skolnik's Radar Handbook, Wehner is far more focused on signal-processing theory and superresolution methods rather than broad radar system design; it complements specialized texts like Steven Kay's Modern Spectral Estimation by applying parametric spectral theory specifically to radar problems.