Why Read This Book

You should read this book if you need a rigorous, focused treatment of sensor-array methods that bridges statistical estimation, spectral analysis, and practical beamforming for radar, sonar, audio and communications. You will learn core array models and classical and optimum processing techniques that remain foundational for modern adaptive and multichannel DSP systems.

Who Will Benefit

Graduate students, research engineers, and experienced practitioners in radar, sonar, wireless communications, and audio who need a deep, theory‑to‑practice treatment of array signal processing.

Level: Advanced — Prerequisites: Undergraduate-level signals and systems, linear algebra (matrix theory and eigenvalues), probability & random processes, and basic digital signal processing (Fourier transforms, digital filter design).

Key Takeaways

• Formulate array signal models and represent array data using steering vectors and array manifolds
• Design and analyze classical and optimum beamformers (e.g., delay‑and‑sum, Capon/MVDR)
• Apply statistical and spectral estimation tools to multi‑sensor data for detection and parameter estimation
• Implement and evaluate adaptive filtering and adaptive beamforming algorithms for interference suppression
• Estimate directions of arrival (DOA) and spatial spectra using periodogram and advanced spectral techniques
• Address practical array issues such as sensor geometry, calibration, wideband effects, and signal propagation

Topics Covered

1. 1. Introduction and Historical Context of Array Processing
2. 2. Physical and Mathematical Models for Sensor Arrays
3. 3. Narrowband and Wideband Array Signal Representations
4. 4. Classical Beamforming: Delay‑and‑Sum and Matched‑Field Methods
5. 5. Optimum Linear Beamforming and the MVDR/Capon Approach
6. 6. Statistical Formulation: Covariance Analysis and Estimation
7. 7. Spectral Analysis for Array Data and FFT Techniques
8. 8. Adaptive Filters and Adaptive Beamforming Algorithms
9. 9. Direction‑of‑Arrival Estimation and Spatial Spectra
10. 10. Detection and Estimation Theory for Arrays
11. 11. Wideband, Broadband, and Frequency‑Domain Array Methods
12. 12. Practical Considerations: Calibration, Coupling, and Noise
13. 13. Applications: Radar, Sonar, Communications, and Audio Arrays
14. Appendices: Mathematical Tools — Linear Algebra, Random Processes, and Transform Methods

Languages, Platforms & Tools

How It Compares

Covers similar foundational ground to Van Trees' work on optimum array processing but is more focused specifically on array methods and their spectral/statistical treatment; complements Haykin's Adaptive Filter Theory by emphasizing spatial processing and array geometry.