Radar tomography is an advanced imaging technique that extends the principles of GPR and other radar systems to create detailed three-dimensional (3D) reconstructions of an object or volume. It borrows concepts from medical imaging (like X-ray Computed Tomography, CT scans) but uses radar waves instead. The goal is to produce volumetric images that reveal the internal structure and composition of the interrogated medium.

Radar tomography involves collecting a large number of radar measurements from multiple angles and positions around the object or volume of interest. These measurements are then processed using specialized reconstruction algorithms to create a 3D image.

1. Multiple Viewing Angles and Positions: Unlike a simple GPR scan that provides a 2D cross-section, tomography requires data acquisition from a wide range of look angles and spatial positions. This is analogous to a medical CT scanner rotating around a patient to get views from all sides.
2. Propagation Modeling: The reconstruction algorithms rely on sophisticated models of how radar waves propagate through the medium, including reflection, refraction, diffraction, and attenuation.
3. Image Reconstruction Algorithms: These algorithms take the multitude of received echoes and mathematically invert the propagation process to infer the distribution of dielectric properties (and sometimes conductivity) within the volume.

Data acquisition for radar tomography is more complex than for simple GPR and requires precise positioning of the radar antennas.
● Synthetic Aperture Radar (SAR) Principles: Many radar tomography systems utilize SAR principles (discussed in Module 6) where a moving antenna simulates a much larger stationary antenna. By moving the radar along a precise path (e.g., linear rail, drone flight path, or human-operated scanner), a synthetic aperture is created, enhancing resolution.
● Multi-Static Arrays: Using multiple transmitting and receiving antennas at different fixed positions around the object. This allows for simultaneous collection of many 'views' without mechanical motion.
● GPR-based Tomography: For subsurface applications, a GPR system can be moved over a grid, or multiple antennas can be deployed at various locations, to acquire the necessary volumetric data.

The reconstruction process is the heart of radar tomography, translating raw radar data into a volumetric representation.
1. Time-Domain Migration: For GPR tomography, a common approach is migration, which repositions reflected energy to its true subsurface location. This converts the two-way travel time to actual depth and compensates for hyperbolic reflection patterns caused by point targets.
2. Frequency-Domain Reconstruction (e.g., Fourier-based methods): Many tomographic algorithms operate in the frequency domain, utilizing the Fourier Slice Theorem (from X-ray CT).
3. Iterative Image Reconstruction: These methods start with an initial guess of the 3D permittivity distribution within the volume and iteratively refine the estimated permittivity distribution until the simulated response closely matches the measured data.

Radar tomography is a powerful tool for obtaining detailed internal structural information in various fields:
● Subsurface Imaging (3D GPR): Creating detailed 3D maps of buried utility networks in complex urban environments.
● Non-Destructive Testing (NDT) and Evaluation: Inspecting concrete structures, timber beams, or masonry walls for internal flaws.
● Security Screening: Developing advanced portal scanners for detecting concealed objects (weapons, explosives) on individuals or within luggage, by creating a 3D internal image.