Why Read This Book

You should read Fourier Array Imaging if you need a rigorous, signal-processing–first treatment of array imaging: you will learn how multi-dimensional system modeling and Fourier/wavenumber processing unify and simplify imaging with coherent arrays. The book gives practical insight into converting array measurements into high-resolution images and provides algorithmic detail (and example code on the included floppy) that make advanced methods implementable.

Who Will Benefit

Graduate students, researchers, and practicing engineers in radar, sonar, acoustic and microwave imaging who want to design or implement Fourier/wavenumber-based array imaging and reconstruction algorithms.

Level: Advanced — Prerequisites: Undergraduate-level signals and systems, strong familiarity with Fourier transforms and linear algebra, basic electromagnetics or wave theory, and familiarity with digital signal processing and numerical computing (e.g., MATLAB or Fortran/C).

Key Takeaways

• Model multi-dimensional coherent wave propagation using system and wavenumber (k‑space) representations
• Apply Fourier-domain processing to array data to perform imaging, focusing, and matched-field processing
• Design and analyze array imaging algorithms for resolution, ambiguity, sampling, and sidelobe behavior
• Implement practical reconstruction schemes (wavenumber filtering, migration, backprojection) and understand their computational tradeoffs
• Account for noise, clutter, and calibration errors in imaging and apply regularization/inversion techniques to improve robustness

Topics Covered

1. 1. Introduction to Array Imaging and Signal-Processing Perspective
2. 2. Fundamentals of Coherent Wave Propagation and System Modeling
3. 3. Spatial Fourier Transform and Wavenumber (k‑Space) Representations
4. 4. Multi‑Dimensional System Theory for Array Measurements
5. 5. Array Geometries, Sampling, and Aliasing in Space and Frequency
6. 6. Fourier‑Domain Imaging: Wavenumber Filtering and Migration
7. 7. Beamforming, Matched‑Field Processing, and Focusing Techniques
8. 8. Inversion, Regularization, and Stability of Imaging Operators
9. 9. Practical Computational Issues and Fast Fourier Techniques
10. 10. Resolution, Ambiguity Functions, and Performance Analysis
11. 11. Calibration, Noise, and Clutter Mitigation in Array Imaging
12. 12. Case Studies and Examples (including code/data on floppy disk)
13. Appendices: Mathematical Background, Numerical Recipes, and Disk Contents

Languages, Platforms & Tools

How It Compares

Covers similar signal-processing ground to Cumming & Wong's SAR processing texts but emphasizes multi-dimensional Fourier/system modeling for general array imaging rather than SAR mission engineering; complements classical radar texts (e.g., Curlander & McDonough) by focusing on k‑space methods and reconstruction theory.