| Project/Area Number |
11450147
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
情報通信工学
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
SATO Toru Kyoto University, Graduate School of Informatics, Professor, 情報学研究科, 教授 (60162450)
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| Co-Investigator(Kenkyū-buntansha) |
KASAHARA Yoshiya Kyoto University, Graduate School of Informatics, Research Associate, 情報学研究科, 助手 (50243051)
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| Project Period (FY) |
1999 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥12,100,000 (Direct Cost: ¥12,100,000)
Fiscal Year 2001: ¥2,800,000 (Direct Cost: ¥2,800,000)
Fiscal Year 2000: ¥4,300,000 (Direct Cost: ¥4,300,000)
Fiscal Year 1999: ¥5,000,000 (Direct Cost: ¥5,000,000)
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| Keywords | subsurface remote sensing / high-resolution radar / radar signal processing / discrete model fitting / inhomogeneous media / 3次元画像化手法 |
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| Research Abstract |
Super-resolution direction finding algorithms inherently assume narrow-band signals, which are not applicable to ultra-wideband radars such as GPR (ground penetration radar).
We have developed imaging algorithms for GPR based on the model fitting principle. A robust and high-resolution imaging algorithm is proposed for retrieving the shape of conductive objects embedded in a uniform lossy and dispersive medium. The target is modeled in terms of the location of of points which represents the outer contour of the object together with the parameters of the medium. A nonlinear least squares fitting is applied to the estimated scattered wave form to adjust the model parameters.
The estimated wave form is computed using the extended ray tracing method which incorporates with the edge diffraction waves. The performance of the algorithm is examined with numerical simulations and test site experiments. The simulation with clutters also revealed the robustness of the algorithm even under a fairly
… More
strong clutter environments.
Noise reduction is another most important issue in the signal processing of subsurface radars. We developed a noise reduction scheme based on a parabolic wavelet transform, which is designed to detect hyperbolic features associated to subsurface radar images. The major limitation of the proposed algorithm was that the parabolic wavelets do not give an orthogonal basis, which limits the reconstruction capability of the algorithm.
We then developed a scheme based on similar wavelet bases, but employing the recursive non-orthogonal decomposition algorithm known as the matching pursuit. The advantage is that the desired signal can be retrieved from a very noisy data if the wave form is included in the dictionary. The inherent problem of this procedure is a heavy computational load because a large number of iteration is needed.
The developed schemes substantially reduce the computation by customizing the algorithm to the signal processing of subsurface radars, and by taking into account the characteristics of the desired signals. The capability of the proposed algorithm in detecting various targets buried in the noise is evaluated based on simulated data for an attenuating and dispersive medium. It is shown that the rough size and the shape of the target can be estimated in the process of this decomposition. Less
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