Effect of antenna dimensions on the antenna footprint in ground penetrating radar applications

Ground penetrating radar (GPR) surveys show that antenna characteristic is strongly influenced by soil conditions. The footprint of the antenna is an important parameter for a good detection result. Various conditions of soil in which a target is buried may change the footprint of the antenna. An antenna with capability to control its footprint is needed in GPR applications. In this study, the authors investigate several ultra-wideband (UWB) antennas with different dimensions to study the effect of antenna dimension on their footprint. Simulation and experiments show that large (small) antenna dimensions result in a large (small) antenna footprint when the observation is located in the near-field region. When the observation is located in the far-field, the footprint of the antenna becomes large (small) if the dimensions of the antenna are small (large). Thus, the size of the antenna footprint can be adjusted by varying the antenna dimension. It is applied in this work to develop a new method for controlling the antenna footprint to deal with varying soil condition. Measurements have been carried out to validate this concept.     

Interferometric SAR image restoration using Monte Carlo metropolismethod

We present a novel method of interferometric synthetic aperture radar (InSAR) image restoration. An InSAR image is modeled as a complex-valued Markov random field (CMRF). Corrupted parts, which are indicated by residues in phase data, are restored by using the Monte Carlo Metropolis (MM) method based on their uncorrupted neighbor's CMRF parameter values. The system is implemented as a complex-valued neural network. The restoration process reduces the residue number, which is useful in the phase unwrapping process. The advantage of the method is demonstrated in the unwrapping process of an InSAR image that contains highly dense residues     

Adaptive noise reduction of InSAR images based on a complex-valuedMRF model and its application t o phase unwrapping problem

We propose a new adaptive noise reduction method for interferometric synthetic aperture radar (InSAR) complex-amplitude images. In the proposed method, we detect residues (singular points) in the phase image as well as their neighbors at first. Normal areas that contain no residue are used for the estimation of correct pixel values at the marked residues according to 5th order non-causal complex-valued Markov random field (CMRF) model. The process is performed block-wise with the assumption of a locally stationary condition of statistics. Using a CMRF lattice complex-valued neural-network, the error energy defined as the squared norm of distance between signal and estimated values is minimized by LMS steepest descent algorithm. Eventually, the number of residues is decreased. An application is also presented. An InSAR image around Mt. Fuji is processed by the proposed technique and then phase-unwrapped by the branch-cut method. It is found that after the application of the proposed method, a better phase unwrapped image can be obtained successfully