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In this paper, an efficient and accurate imaging algorithm is presented for Ground-Based Synthetic Aperture Radar (GB-SAR) or other radar systems that could be formed by a physical or synthetic linear aperture. The imaging algorithm is based on the fractional Fourier transform for the azimuth compression. A mathematical framework is derived according to the projection of a sample reflectivity image onto the pseudopolar coordinate and its implementation was presented. With the data acquisition geometry and the pseudopolar imaging coordinate, the phase of a point target can be expressed as a quadratic phase exponential. It makes that only one-dimensional fractional Fourier transform is needed for the azimuth compression of the time domain backscatter data for the GB-SAR imaging problem. By further research, the optimal transformation order which represents the spatial frequency changes by the fractional Fourier transform was given subsequently. Taking advantage of this optimal representation, the proposed approach avoids the large calculation that occurs in the time domain back projection (TDBP). Comparing to the far-field pseudopolar format algorithm (FPFA), the accuracy of the proposed algorithm is much improved. Meanwhile, the proposed approach holds the almost same computational cost and complexity as the FPFA. The proposed approach keeps the advantages of the imaging quality of the TDBP and the computational cost of the FPFA that are two important aspects of the GB-SAR applications. Both the numerical simulation and the field GB-SAR experiment show that the algorithm is more suitable for the high precision GBSAR imaging, especially for the near-field.

We propose a high-resolution velocity analysis method to estimate the electromagnetic wave propagation velocity in subsurface medium. The estimation is achieved by applying the l-1 norm regularized least-squares method to the conventional common-midpoint (CMP) velocity analysis algorithm. The proposed method can provide not only higher resolution than the conventional velocity analysis method, but can also be applied with a coarse sampling array system, such as our array ground penetrating radar YAKUMO, which returns eight CMP traces within a two meter width. The main purpose of this approach is for precise pavement inspection at shallow depths. We applied this method to both a simulated dataset and real data acquired by YAKUMO at a model airport taxiway to detect the slight velocity changes caused by millimeter-thin cracks filled with air or water within the 15 cm-thick asphalt pavement. In both cases slight velocity changes of about 0.005 m/ns can be detected, and the difference between air- and water-filled cracks can be distinguished. Also, this method is applied to a data acquired at airport taxi-way, the damaged parts are detected successfully and shows good agreement with the corning results. The results indicate that the proposed method is effective for pavement inspection, especially in the presence of thin cracks that cannot be seen directly with the reflected signal.

To provide meaningful data, polarimetric ground penetrating radar (GPR) requires accurate calibration of channel imbalance and crosstalk not only in the amplitude term but also in the phase term. Currently, calibration techniques usually ignore higher-order terms to simplify equations. These techniques, though very easy to perform, provide less accurate calibration results for the crosstalk. To improve the accuracy of calibration, we have derived a new mathematical formulation to calibrate a polarimetric GPR system. We measured several scattering matrices to obtain the necessary calibration parameters. The calibration technique was tested from measurements conducted on a 22.5° oriented dihedral corner reflector from 1 to 6 GHz with a step of 0.1 GHz. The calibrated results are found to be consistent with the theoretically values, especially for the crosstalk.