PRACTICAL ARRAY PROCESSING
A Complete Education on Array Processing-from Theory to Practice
Learn all the ins and outs of creating reliable communication systems with Practical Array Processing. This comprehensive guide goes through the entire subject, from a detailed look at the theory of array processing to practical information on how to design and build systems for both commercial and military applications.
Written by a recognized expert in the field, Practical Array Processing focuses on the practical issues in any project involving one or more antenna arrays. It provides key background information and hands-on techniques regarding array calibration…antenna array design…signal detection…direction finding…interference cancellation…and more. The book concludes with an explanation of how emitters can be located using angles of arrival or time differences measured at multiple sites.
Practical Array Processing includes
- Hands-on advice for performing full systems engineering analysis on array processing systems
- Coverage of basic techniques, such as calibration, not included in most texts
- Easy-to-understand mathematical algorithms, with software and firmware applications
- In-depth, accessible discussions of array processing theory
In this array processing guide
• Digital Receiver Techniques • Array Signal Processing Models • Signal Detection • Direction Finding • Beamforming • Emitter Geolocation
Why Read This Book
You will get a practical, start‑to‑finish education in array signal processing that bridges rigorous theory and real engineering practice, with emphasis on calibration, beamforming, and interference suppression. The book teaches not just algorithms (MUSIC, ESPRIT, adaptive beamformers) but how to make them work on real radar, sonar, and communications systems under imperfect conditions.
Who Will Benefit
Practicing signal processing or RF engineers and senior graduate students who need to design, build, or evaluate antenna/sensor array systems for radar, communications, or audio applications.
Level: Intermediate — Prerequisites: Undergraduate-level linear algebra and signals & systems, basic probability/statistics (random processes), familiarity with digital signal processing concepts, and some experience with MATLAB or equivalent numeric tools.
Key Takeaways
- Design array geometries and element layouts for target detection, resolution, and coverage tradeoffs
- Implement and tune direction‑of‑arrival (DOA) algorithms such as MUSIC and ESPRIT for practical datasets
- Calibrate arrays and compensate for element errors, mutual coupling, and sensor nonidealities
- Apply adaptive beamforming and interference cancellation techniques to improve SINR in real environments
- Integrate array processing methods into radar, sonar, and communications systems with attention to hardware and software implementation
- Evaluate performance using statistical signal processing tools (CRB, detection theory, and spectral analysis)
Topics Covered
- 1. Introduction to Array Processing: Concepts and Applications
- 2. Signal and Array Models: Narrowband and Wideband Formulations
- 3. Array Geometry, Element Patterns, and Mutual Coupling
- 4. Basics of Beamforming and Spatial Filtering
- 5. Direction‑of‑Arrival Estimation: Classical and Subspace Methods (MUSIC, ESPRIT)
- 6. Array Calibration and Error Compensation Techniques
- 7. Adaptive Arrays and Interference Cancellation (LMS, RLS, MVDR, GSC)
- 8. Detection, Estimation, and Statistical Performance (CRB, Hypothesis Testing)
- 9. Wideband and Time‑Delay Beamforming, Beamspace Methods
- 10. MIMO and Multistatic Array Concepts for Communications and Radar
- 11. Implementation Considerations: Sampling, FFTs, Real‑Time Processing, and Hardware
- 12. Case Studies: Radar, Sonar, Speech/Audio, and Wireless Communications
- 13. Practical Measurement, Validation, and Testbed Techniques
- Appendices: Mathematical Background, Fast Algorithms, and Reference Tables
Languages, Platforms & Tools
How It Compares
More application‑focused than Van Trees' Optimum Array Processing (which is heavier on estimation theory) and more array‑specific than Haykin's Adaptive Filter Theory; this book emphasizes practical calibration, implementation, and case studies.
