SAR Signal Processing with MatLab Algorithms - Reloaded

Upgrade and extension of m-scripts for M. Soumekh's book "Synthetic Aperture Radar Signal Processing with MatLab Algorithms".

This contribution aims to make more straightforward and intelligible the scripts provided with professor M. Soumekh book from late 90's. The following improvements were incorporated:

1. Separation and modularization of the 4 target aerea reconstruction algorithms provided in the book: 2D Matched Filtering (Wavefront Reconstruction), Time Domain Correlation (TDC), Back-Projection (BP) and Range Stacking (RS).
2. Replacement of obsolete graphics commands with modern rendering commands, clearly showing the effect of DSP operations on SAR signals.
3. Removal of clumsy code for 2D-interpolation of unevenly spaced data in (kx,ky)-domain and replacement with modern MatLab commands that are less cumbersome and more elegant.
4. Assigned proper names to important SAR signals following the book notation and nomenclature as much as possible. Also, I provide a detailed table of all these SAR signals together with their domains.
5. Added several pictures from Chapter 2.
6. Added a couple of scripts computing the CTFT of AM-PM and PM Spherical SAR Signal, both numerically and by use of the Stationary Phase Approximation (SPA) method.
7. Several minor code improvements.
8. For the first time since 1999, complete implementation of CSAR reconstruction algorithms (full/partial scan, curved/planar antenna, 2D/3D version):

• 2D Matched Filtering (Wavefront Reconstruction)
• Time Domain Correlation (TDC) Algorithm
• Back-Projection (BP) Algorithm.

9. Green Function Study for CSAR signal processing.

10. Reproduction of Radar Radiation Pattern figures of Chapter 3 (planar, circular, parabolic).

11. Accurate expression for the approximated analytical slow-time Fourier Transform via the Stationary Phase Approximation (SPA)

method.

12. Accurate numerical computation of the slow-time Fourier Transforms of the PM and AM-PM spherical signals.

13. Addition of several scripts demonstrating the theory of Chapter 8 dedicated to block-based subspace processing for detecting change or target registration.

14. Pulse Compression via Matched Filtering in frequency domain and Cross-Correlation in time domain demos.

15. Added several moving picture demos in .gif format.

Here I provide scripts for the theory covered in Ch. 1, 2, 3, 5, 6, 7 and 8.