Array Signal Processing: Concepts and Techniques
This is the first book on the market to bring together material on array signal processing in a coherent fashion, with uniform notation and convention of models. KEY TOPICS: Using extensive examples and problems, it presents not only the theories of propagating waves and conventional array processing algorithms, but also the underlying ideas of adaptive array processing and multi-array tracking algorithms. This manual will be valuable to engineers who wish to practice and advance their careers in the array signal processing field.
Why Read This Book
You should read this book if you want a coherent, example-driven introduction to array signal processing that ties physical wave-propagation models to practical algorithms (beamformers, subspace DOA methods, adaptive arrays, and tracking). It balances theory and worked problems so you can move from understanding array manifolds and performance bounds to implementing MUSIC/ESPRIT-style estimators and adaptive beamformers in practice.
Who Will Benefit
Graduate students and practicing engineers working on radar, sonar, wireless communications, or array-based sensing who need a solid foundation in beamforming, DOA estimation, and adaptive array techniques.
Level: Advanced — Prerequisites: Linear algebra (eigen-decomposition), basic DSP (Fourier transforms, sampling), probability/statistics (estimation basics), and Signals & Systems fundamentals.
Key Takeaways
- Model array measurements using plane-wave/steering-vector formulations and understand the array manifold concept.
- Design and analyze conventional and minimum-variance beamformers (e.g., Bartlett, Capon/ MVDR, LCMV).
- Implement and apply subspace DOA estimation methods such as MUSIC and related eigenstructure techniques (and understand their limitations).
- Evaluate estimator and beamformer performance using Cramér–Rao bounds, sample covariance effects, and finite-snapshot considerations.
- Apply adaptive-array algorithms and multi-array tracking approaches, including practical issues like calibration and sensor errors.
Topics Covered
- 1. Introduction to Array Signal Processing and Applications
- 2. Array Notation, Geometry, and Wave Propagation Models
- 3. Array Manifold and Steering Vectors
- 4. Covariance Modeling and Sample Statistics
- 5. Conventional Beamforming: Delay-and-Sum and Bartlett
- 6. Adaptive Beamforming: MVDR/Capon and LCMV Methods
- 7. Subspace Methods for Direction Finding (MUSIC/ESPIRIT and variants)
- 8. Maximum Likelihood and Parametric Estimation Techniques
- 9. Performance Analysis and Cramér–Rao Bounds
- 10. Tracking and Multi-Array Processing Algorithms
- 11. Calibration, Sensor Errors, and Practical Considerations
- 12. Examples, Numerical Results, and Problems
- Appendices: Relevant Linear Algebra and Signal Processing Background
Languages, Platforms & Tools
How It Compares
Covers similar practical/algorithmic ground to Monzingo & Miller's Introduction to Adaptive Arrays but is broader in DOA/subspace and tracking topics; Van Trees (Optimum Array Processing / Detection & Estimation) is more rigorous/estimation-theory heavy while Johnson provides a more accessible, example-focused treatment.
