Application of Support Vector Machines for Estimating Wall Parameters in Through-Wall Radar Imaging
In through-wall radar imaging (TWRI), ambiguities in wall characteristics including the thickness and the relative permittivity will distort the image and shift the imaged target position. To quickly and accurately estimate the wall parameters, an approach based on a support vector machine (SVM) is...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2015/456123 |
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| author | Hua-Mei Zhang Ye-Rong Zhang Fang-Fang Wang Jun-Lin An |
| author_facet | Hua-Mei Zhang Ye-Rong Zhang Fang-Fang Wang Jun-Lin An |
| author_sort | Hua-Mei Zhang |
| collection | DOAJ |
| description | In through-wall radar imaging (TWRI), ambiguities in wall characteristics including the thickness and the relative permittivity will distort the image and shift the imaged target position. To quickly and accurately estimate the wall parameters, an approach based on a support vector machine (SVM) is proposed. In TWRI problem, the nonlinearity is embodied in the relationship between backscatter data and the wall parameters, which can be obtained through the SVM training process. Measurement results reveal that once the training phase is completed, the technique only needs no more than one second to estimate wall parameters with acceptable errors. Then through-wall images are reconstructed using a back-projection (BP) algorithm by a finite-difference time-domain (FDTD) simulation. Noiseless and noisy measurements are discussed; the simulation results demonstrate that noisy contamination has little influence on the imaging quality. Furthermore, the feasibility and the validity are tested by a more realistic situation. The results show that high-quality and focused images are obtained regardless of the errors in the wall parameter estimates. |
| format | Article |
| id | doaj-art-ea44eb03921a42a6976ce64ee3b7cff7 |
| institution | Kabale University |
| issn | 1687-5869 1687-5877 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-ea44eb03921a42a6976ce64ee3b7cff72025-02-03T01:27:17ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772015-01-01201510.1155/2015/456123456123Application of Support Vector Machines for Estimating Wall Parameters in Through-Wall Radar ImagingHua-Mei Zhang0Ye-Rong Zhang1Fang-Fang Wang2Jun-Lin An3School of Electronic Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Electronic Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Electronic Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaInstitute of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaIn through-wall radar imaging (TWRI), ambiguities in wall characteristics including the thickness and the relative permittivity will distort the image and shift the imaged target position. To quickly and accurately estimate the wall parameters, an approach based on a support vector machine (SVM) is proposed. In TWRI problem, the nonlinearity is embodied in the relationship between backscatter data and the wall parameters, which can be obtained through the SVM training process. Measurement results reveal that once the training phase is completed, the technique only needs no more than one second to estimate wall parameters with acceptable errors. Then through-wall images are reconstructed using a back-projection (BP) algorithm by a finite-difference time-domain (FDTD) simulation. Noiseless and noisy measurements are discussed; the simulation results demonstrate that noisy contamination has little influence on the imaging quality. Furthermore, the feasibility and the validity are tested by a more realistic situation. The results show that high-quality and focused images are obtained regardless of the errors in the wall parameter estimates.http://dx.doi.org/10.1155/2015/456123 |
| spellingShingle | Hua-Mei Zhang Ye-Rong Zhang Fang-Fang Wang Jun-Lin An Application of Support Vector Machines for Estimating Wall Parameters in Through-Wall Radar Imaging International Journal of Antennas and Propagation |
| title | Application of Support Vector Machines for Estimating Wall Parameters in Through-Wall Radar Imaging |
| title_full | Application of Support Vector Machines for Estimating Wall Parameters in Through-Wall Radar Imaging |
| title_fullStr | Application of Support Vector Machines for Estimating Wall Parameters in Through-Wall Radar Imaging |
| title_full_unstemmed | Application of Support Vector Machines for Estimating Wall Parameters in Through-Wall Radar Imaging |
| title_short | Application of Support Vector Machines for Estimating Wall Parameters in Through-Wall Radar Imaging |
| title_sort | application of support vector machines for estimating wall parameters in through wall radar imaging |
| url | http://dx.doi.org/10.1155/2015/456123 |
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