Three-dimensional Image Reconstruction for Subsurface Radar Data Processing

地下雷达数据处理的三维图像重建

• 批准号: 11450147
• 负责人: SATO Toru
• 金额: $ 7.74万
• 依托单位: KYOTO UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 1999
• 资助国家: 日本
• 起止时间: 1999 至 2001
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-11450147/
• 关键词: subsurface remote sensing; high-resolution radar; radar signal processing; discrete model fitting; inhomogeneous media; 3次元画像化手法

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

针对超分辨测向算法固有的假设窄带信号不适用于探地雷达等超宽带雷达的特点，提出了基于模型拟合原理的超宽带雷达成像算法。提出了一种稳健的高分辨率成像算法，用于提取均匀有损和色散介质中的导电物体的形状。根据代表物体外轮廓的点的位置以及介质的参数来对目标进行建模。对估计的散射波形进行非线性最小二乘拟合法调整模型参数，结合边缘绕射波的扩展射线追踪法计算估计的波形。通过数值模拟和现场实验验证了该算法的性能。在杂波环境下的仿真结果也表明，即使在…较高的情况下，该算法也具有较强的鲁棒性噪声抑制是地下雷达信号处理中的另一个最重要的问题。我们提出了一种基于抛物线小波变换的降噪方案，该方案旨在检测与地下雷达图像相关的双曲线特征。该算法的主要缺陷是抛物线小波不给出正交基，这限制了算法的重构能力，在此基础上提出了一种基于相似小波基的方案，但采用了递归的非正交分解算法，称为匹配追踪。优点在于，如果将波形包括在字典中，则可以从非常噪声的数据中检索所需的信号。这一过程的固有问题是计算量大，因为需要大量的迭代，通过定制用于地下雷达信号处理的算法并考虑所需信号的特性，所开发的方案大大减少了计算量。基于衰减和色散介质的仿真数据，评估了所提出的算法对隐藏在噪声中的各种目标的检测能力。结果表明，在这种分解过程中可以估计出目标的粗大小和形状。较少

项目成果

Y.Kasahara: "A Flexible Modeling of Global Plasma Profile Deduced from Wave Data"Progress in Discovery Science, Lecture Notes in Computer Science. (in press). (2002)

Y.Kasahara：“从波数据推导的全球等离子体剖面的灵活建模”发现科学进展，计算机科学讲义。

T.Sato: "Noise reduction and identification of subsurface radar images using recursive wavelet decomposition"Proc. 2000 IEEE Int. Geosci. Remote Sens. Symp. . 660-662 (2000)

T.Sato：“使用递归小波分解对地下雷达图像进行降噪和识别”Proc。

T.Sato: "Space debris measurements in Japan"Adv. Space Res.. 23. 55-65 (1999)

T.Sato：“日本的空间碎片测量”Adv.

T. Sato, Y. Tada: "Noise reduction and identification of subsurface radar images using recursive wavelet decomposition"Proc. 2000 IEEE Int. Geosci. Remote Sens. Symp.. 660-662 (2000)

T. Sato，Y. Tada：“使用递归小波分解对地下雷达图像进行降噪和识别”Proc。

Y.Goto, Y.Kasahara, T.Sato: "Study on direction finding method using wave distribution function with Gaussian distribution model"IEICE Trans. Commun.. Vol. J84-B, No.2(in Japanese). 263-271 (2001)

Y.Goto、Y.Kasahara、T.Sato：“利用高斯分布模型的波分布函数测向方法的研究”IEICE Trans。