Synthetic Aperture Radar Signal Processing with MATLAB Algorithms
An up-to-date analysis of the SAR wavefront reconstruction signal theory and its digital implementation With the advent of fast computing and digital information processing techniques, synthetic aperture radar (SAR) technology has become both more powerful and more accurate. Synthetic Aperture Radar Signal Processing with MATLAB Algorithms addresses these recent developments, providing a complete, up-to-date analysis of SAR and its associated digital signal processing algorithms. This book introduces the wavefront reconstruction signal theory that underlies the best SAR imaging methods and provides clear guidelines to system design, implementation, and applications in diverse areas-from airborne reconnaissance to topographic imaging of ocean floors to surveillance and air traffic control to medical imaging techniques, and numerous others. Enabling professionals in radar signal and image processing to use synthetic aperture technology to its fullest potential, this work: Includes M-files to supplement this book that can be retrieved from The MathWorks anonymous FTP server at ftp://ftp.mathworks.com/pub/books/soumekh Provides practical examples and results from real SAR, ISAR, and CSAR databases Outlines unique properties of the SAR signal that cannot be found in other information processing systems Examines spotlight SAR, stripmap SAR, circular SAR, and monopulse SAR modalities Discusses classical SAR processing issues such as motion compensation and radar calibration
Why Read This Book
You will get a mathematically rigorous yet practical presentation of modern SAR imaging methods together with MATLAB implementations so you can go from theory to working code. The book emphasizes wavefront-reconstruction approaches (backprojection, Omega‑K, chirp‑scaling) and system design issues that let you evaluate resolution, sampling and processing tradeoffs for real SAR systems.
Who Will Benefit
Graduate students, researchers, and practicing radar/DSP engineers who need a deep understanding of SAR imaging algorithms and MATLAB reference implementations.
Level: Advanced — Prerequisites: Linear systems and transforms (Fourier theory), complex signal processing, basic radar concepts (range/Doppler geometry), and familiarity with MATLAB.
Key Takeaways
- Implement common SAR imaging algorithms in MATLAB including backprojection, Range‑Doppler, Omega‑K (Stolt), and chirp‑scaling
- Analyze and apply wavefront‑reconstruction theory to derive and choose appropriate imaging methods
- Evaluate resolution, sampling, and aliasing tradeoffs for SAR system design
- Apply motion compensation and autofocus concepts to improve image quality
- Design and optimize matched filters and pulse compression for SAR range processing
- Understand implementation issues such as interpolation/stolt remapping, windowing, and efficient FFT usage
Topics Covered
- Introduction to SAR and imaging geometry
- Signal model and wavefront reconstruction fundamentals
- Range compression and matched filtering
- Azimuth processing and Doppler history
- Range‑Doppler algorithm and its approximations
- Chirp‑scaling algorithm
- Omega‑K (Stolt) algorithm and range migration correction
- Backprojection and spotlight SAR imaging
- Motion compensation and autofocus techniques
- Resolution, sampling, and system design considerations
- Practical MATLAB implementations and examples
- Appendices: Fourier transforms, interpolation, and numerical issues
Languages, Platforms & Tools
How It Compares
Covers similar mathematical and algorithmic ground to Curlander & McDonough's SAR text but with more emphasis on wavefront‑reconstruction theory and practical MATLAB code; Carrara et al. is more focused on spotlight SAR and implementation examples.
