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Published on: February 12, 2014
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A Novel, Efficient Algorithm for Subsurface Radar Imaging below a Non-Planar Surface.
Ingrid Ullmann1, Martin Vossiek1
1Institute of Microwaves and Photonics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany.
Sensors (Basel, Switzerland)
|November 25, 2023
Summary
This study introduces a novel radar imaging concept for complex subsurface environments. It enhances computational efficiency for irregularly shaped objects by combining spatial and wavenumber domain (k-space) reconstruction methods.
Area of Science:
- Radar Imaging
- Electromagnetic Wave Propagation
- Signal Processing
Background:
- Classical radar imaging often assumes free-space propagation, which is inadequate for applications like ground penetrating radar (GPR) or non-destructive testing (NDT).
- Subsurface image reconstruction in multi-material backgrounds is complex, with spatial domain methods (e.g., Backprojection) being computationally intensive for non-planar objects.
- Wavenumber domain (k-space) reconstruction is efficient for planar surfaces, but many real-world scenarios involve partly planar geometries.
Purpose of the Study:
- To develop a novel radar imaging concept for efficiently reconstructing subsurface objects with partly planar surfaces.
- To leverage the computational advantages of k-space reconstruction for complex geometries.
- To improve the overall computational efficiency of subsurface imaging for irregularly shaped targets.
Main Methods:
- A novel concept is introduced that superposes sub-images, reconstructing as much as possible in the wavenumber domain (k-space).
- A segmentation scheme is developed to identify image parts suitable for k-space reconstruction.
- The method is validated using monostatic synthetic aperture radar (SAR) and multiple-input-multiple-output (MIMO) radar data.
Main Results:
- The proposed algorithm effectively reconstructs partly planar surfaces by combining k-space and spatial domain techniques.
- The segmentation scheme successfully determines optimal reconstruction domains for different image parts.
- Experimental results demonstrate significant augmentation in computational efficiency for imaging irregularly shaped geometries.
Conclusions:
- The novel k-space-based approach offers a significant improvement in computational efficiency for subsurface radar imaging of complex geometries.
- This method provides a more efficient alternative to traditional spatial domain algorithms for partly planar scenarios.
- The findings are applicable to various radar imaging applications requiring subsurface analysis.
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