A novel 3-D subsurface radar imaging technique

The problem of the formation of subsurface images using stand-off forward looking radar is by far more severe than that of forming the radar images in the free-space. A subsurface image needs to be accurately focused taking into account both the refraction and dispersion of the wavefield. This paper presents a novel imaging algorithm specially tailored for subsurface sensing. A simple and effective characterization technique for the retrieval of the dielectric permittivity is outlined. The proposed soil characterization and subsurface imaging techniques are validated experimentally. Results show that the geometric distortion in the subsurface images due to the refraction and dispersion of the wavefields is successfully corrected     

Advanced subsurface radar system for imaging buried pipes

A subsurface radar system for imaging buried pipes was developed. The system is capable of reconstructing clear pipe images under unfavorable conditions, such as large attenuation rate of the radio waves propagating in soil. The output power of a pulse generator, the amplifier gain, and average number are controlled by moving the observation depth to improve the detection capability. Fast image processing methods are also used. A 6.5-cm-diameter steel pipe, buried at a depth of 2.5 m, was clearly reconstructed as a color image averaged attenuation rate of 12.6 dB/m in the soil. A plastic pipe (6.5 cm in diameter), buried at a depth of 1 m, was also imaged by the system     

A quality controllable multi-view object reconstruction method for 3D imaging systems

This paper addresses a novel multi-view visual hull mesh reconstruction for 3D imaging with a system quality control capability. There are numerous 3D imaging methods including multi-view stereo algorithms and various visual hull/octree reconstruction methods known as modeling from silhouettes. The octree based reconstruction methods are conceptually simple to implement, while encountering a conflict between model accuracy and memory size. Since the tree depth is discrete, the system performance measures (in terms of accuracy, memory size, and computation time) are generally varying rapidly with the pre-specified tree depth. This jumping system performance is not suitable for practical applications; a desirable 3D reconstruction method must have a finer control over the system performance. The proposed method aims at the visual quality control along with better management of memory size and computation time. Furthermore, dynamic object modeling is made possible by the new method. Also, progressive transmission of the reconstructed model from coarse to fine is provided. The reconstruction accuracy of the 3D model acquired is measured by the exclusive OR (XOR) projection error between the pairs of binary images: the reconstructed silhouettes and the true silhouettes in the multiple views. Interesting properties of the new method and experimental comparisons with other existing methods are reported. The performance comparisons are made under either a comparable silhouette inconsistency or a similar triangle number of the mesh model. It is shown that under either condition the new method requires less memory size and less computation time.     

Microwave imaging of subsurface cylindrical scatters from crosspolar backscatter

A technique based on polarisation discrimination is shown to produce microwave images of suitably depolarising subsurface targets which are superior to copolar images by virtue of rejecting the rough-surface return.     

Efficient 3D imaging method of MIMO radar for moving target

When compressive sensing (CS) was used to achieve sparse imaging of moving targets,the Doppler frequency caused by motion will increase the processing dimension,change the center frequency of echo and worsen the mutual coherence property of measurement matrix.In order to improve the three-dimensional (3D) imaging performance of MIMO radar for moving targets,an efficient method was proposed.In each dimension,the distribution information of targets was searched respectively and a new low-dimensional measurement matrix was reconstructed accordingly,so the targets’ area was narrowed down.At the same time,in order to optimize the mutual coherence property of measurement matrix,Bayesian method was used to optimize the velocity-dimensional projection matrix to reduce the strong mutual coherence brought by sampling of Doppler frequency,then the efficient sparse imaging could be achieved.The simulation results show that proposed method can improve the efficiency,accurate imaging performance with efficient.     

A characterization of subsurface radar targets

The capability of subsurface target identification at shallow depths has been demonstrated using an electromagnetic video or base-band pulse radar. Real radar measurements were collected for five targets at a depth of 5 cm (2 in) in various ground conditions. These measurements were processed for target characterization and identification. Identification performance based on a single radar observation was evalualted. The identification process requires only simple algebraic operations and thus offers the potential of real-time on-location identification of subsurface targets.     

A fast wavelet-based reconstruction method for magnetic resonance imaging

In this work, we exploit the fact that wavelets can represent magnetic resonance images well, with relatively few coefficients. We use this property to improve magnetic resonance imaging (MRI) reconstructions from undersampled data with arbitrary k-space trajectories. Reconstruction is posed as an optimization problem that could be solved with the iterative shrinkage/thresholding algorithm (ISTA) which, unfortunately, converges slowly. To make the approach more practical, we propose a variant that combines recent improvements in convex optimization and that can be tuned to a given specific k-space trajectory. We present a mathematical analysis that explains the performance of the algorithms. Using simulated and in vivo data, we show that our nonlinear method is fast, as it accelerates ISTA by almost two orders of magnitude. We also show that it remains competitive with TV regularization in terms of image quality.     

基于穿墙雷达的虚拟多视角成像

由于墙体及室内目标的存在,穿墙雷达回波信号中包含许多多径分量,导致在成像后图像中存在许多幻影分量.在传统的穿墙雷达成像及预处理中,将多径分量视为杂波和干扰分量,并提出许多方法来抑制多径分量.基于多径分量提供了同一目标不同观测视角的优势,提出了一种基于室内电波传播模型的多径利用方法.该方法在后向投影成像方法的基础上,利用双圆解析表达式方法识别图像中幻影的位置信息,将幻影与目标相关联,并结合指数加权函数的方法,实现目标的虚拟多视角成像,仿真实测结果验证了该方法的有效性.     

Time-domain inverse scattering method for cross-borehole radar imaging

In this paper, we consider a solution method of the inverse problem of imaging two-dimensional (2D) objects buried underground by cross-hole radar data in the time domain. In addition to less information on the targets due to restriction on the arrangement of transmitters and receivers than for full-view cases such as imaging of objects in free space, the large search region between boreholes makes solving the inverse problem difficult. Although iterative optimization approaches take long computing time, these approaches give much better image qualities for high-contrast objects than linear inversions such as a diffraction tomography. However, the reconstruction in a large search region with limited-view measurements often fails trapped in a local minimum. To overcome this difficulty, we propose a two-step iterative approach: the first step is to reduce the search region to a smaller one and the second step is the accurate reconstruction of the targets in the small region. Both steps are based on an iterative optimization approach, i.e., the forward-backward time-stepping method previously proposed. This two-step approach is tested for detection of tunnel-like objects surrounded by a heterogeneous background medium to evaluate its performance. Numerical results indicate the efficiency of the approach and its ability of circumventing local minima.     

Statistical method to detect subsurface objects using arrayground-penetrating radar data

We introduce a combination of high-dimensional analysis of variance (HANOVA) and sequential probability ratio test (SPRT) to detect buried objects from an array ground-penetrating radar (GPR) surveying a region of interest in a progressive manner. Using HANOVA, we exploit the transient characteristic of GPR signals in the time domain to extract information about buried objects at fixed positions of the array. Based on the output of the HANOVA, the SPRT is employed to make detection decisions recursively as the array moves downtrack. The method is on-line implementable and of low computational complexity. Our approach is validated using field-data from two quite different GPR sensing systems designed for landmine detection applications     

一种0.34 THz站开式三维成像安检仪

介绍了一种太赫兹站开式大视角三维成像雷达系统。系统可用于人体隐藏危险品的成像检测,采用二维非均匀逐点扫描的工作方式,视场范围达到0.6 m×1 m,成像速度达到2 s。系统工作于0.34 THz,采用直径450 mm椭球镜聚焦,在5.8 m的成像距离实现了2 cm的方位分辨力。为了改善回波动态范围,采用相位噪声相消的系统结构。系统采用调频连续波(FMCW)雷达线性调频信号作为工作波形,工作带宽达到12 GHz。成像结果显示,系统校正后的距离分辨力可达到2 cm,能够实现隐藏危险品的有效检测。     

Prediction-correction method for electromagnetic imaging withcurrent reconstruction

A prediction-correction methodology is presented for improving the numerical stability of the iterative process involved in the solution of nonlinear inverse scattering problems related to the electromagnetic imaging of dielectric objects. In various methods developed so far, each iteration is performed by only using the results obtained in the preceding iteration. The results computed in the preceding iteration along with those acquired in previous iterations are employed selectively to make a prediction of the object function to be used in the next iteration. As a consequence, the numerical stability and the convergence of the iterative process are improved. This is illustrated by numerical computation results obtained for lossless and for lossy dielectric bodies     

Fast 3-d tomographic microwave imaging for breast cancer detection

Microwave breast imaging (using electromagnetic waves of frequencies around 1 GHz) has mostly remained at the research level for the past decade, gaining little clinical acceptance. The major hurdles limiting patient use are both at the hardware level (challenges in collecting accurate and noncorrupted data) and software level (often plagued by unrealistic reconstruction times in the tens of hours). In this paper we report improvements that address both issues. First, the hardware is able to measure signals down to levels compatible with sub-centimeter image resolution while keeping an exam time under 2 min. Second, the software overcomes the enormous time burden and produces similarly accurate images in less than 20 min. The combination of the new hardware and software allows us to produce and report here the first clinical 3-D microwave tomographic images of the breast. Two clinical examples are selected out of 400+ exams conducted at the Dartmouth Hitchcock Medical Center (Lebanon, NH). The first example demonstrates the potential usefulness of our system for breast cancer screening while the second example focuses on therapy monitoring.     

A three-parameter mechanical property reconstruction method for MR-based elastic property imaging

A reconstruction process featuring full parameterization of the three dimensional, time-harmonic equations of linear elasticity is developed and reconstructed property images are presented from simulation-based investigation. While interesting in its own right through the potential for increased adaptability of these reconstructive elastic imaging techniques, this study also presents a set of analysis tools used to study the poor convergence behavior found in the case of tissue like conditions (i.e. nearly incompressible materials). The choice of elastic properties for imaging in elastography research remains an open question at this point; the use of the stability and sensitivity-based analytical methods described here will help to predict and understand the value and reliability of different parameterizations of elasticity imaging. Additionally, though results indicate significant work needs to be done to achieve effective multiparameter reconstructive imaging, the methods detailed here offer the promise of increased flexibility and sophistication in elastographic imaging techniques.     

非均匀线阵多输入多输出雷达成像旁瓣抑方法

针对非均匀线阵多输入多输出(Multiple Input Multiple Output,MIMO)雷达空间采样率低、成像旁瓣较高的问题,提出了一种基于阵列内插技术的非均匀线阵MIMO雷达成像旁瓣抑制方法. 对匹配滤波后MIMO雷达形成的非均匀等效阵列进行虚拟内插处理,将其变换为均匀线阵,在此基础上,再采用反向投影(Back Projection,BP)算法进行成像. 理论分析和仿真结果表明：所提方法有效地降低了非均匀线阵MIMO雷达的旁瓣水平,提高了成像质量.      

Developing experimental models for a bistatic subsurface radar

The letter presents preliminary results from a recent experimental investigation to characterise the electromagnetic scattering from conducting objects submerged in a dissipative half-space. The variation of the magnetic field with respect to the depth of the scattering object is discussed. Correlations are also sought between the measured field components and the size of the scattering object.     

基于距离多普勒概念的全息雷达成像算法

全息雷达成像系统具有电离辐射小、穿透衣服等优点,在人体安检等领域具有广泛的应用前景.然而,现有系统尚存在多运动目标补偿及快速成像能力不足的问题,限制了其在火车站等人流量大的场景中的应用.本文提出了一种基于距离多普勒概念的全息雷达成像方法,其优点是成像速度快、运动补偿方便,具有多运动目标快速成像的潜力.该方法利用了信号在时间采样和空间采样上的对称性,将合成孔径雷达成像中常用的距离多普勒算法引入全息雷达成像.本文对距离多普勒全息雷达成像算法进行了推导,通过仿真及试验,验证了该成像方法具有多目标运动补偿及快速成像的能力.     

一种快速三维重建评估算法

任何一种三维重建算法都必须对其优劣进行定量评估。不同的三维重建算法所采取的世界坐标系不同,所重建出的景物的大小、位置、姿态也不同,无法直接与标准库给出的精确测量值进行比对。提出了一种新的算法,利用Anders Dahl等人建立的多视点标准数据库里的摄像机投影矩阵,通过最小二乘拟合,直接估算出从算法自身的世界坐标到标准库世界坐标的变换矩阵,再稍作优化,然后统计计算重建精度和覆盖度,从而简化了评估过程。     

基于支持向量机的穿墙雷达目标形状重构方法

为解决超宽带穿墙雷达中目标成像问题,提出一种后向投影算法和支持向量机(Support Vector Machine,SVM)相结合的方法.该方法通过BP算法得到穿墙成像数据,再利用SVM对数据进行分类,成功地解决了穿墙成像中的目标定位和形状识别问题.利用穿墙模型实验数据的仿真结果验证了该方法的可行性和有效性.测试结果表明:该方法能对墙后未知目标实现形状重构,且具有极高的空间分辨率;此外,当信号被噪声污染时,该方法也能很好对墙后目标形状进行预测,体现了该方法的鲁棒性.最后对不同采样长度和空间采样间隔的分析表明,采样长度和采样间隔对目标形状识别的影响有限,采样位置数的增加、采样间隔的减小更有利于提高目标的分类准确率.     

Time-domain imaging of radar targets using sinogram restoration forlimited-view reconstruction

The time-domain image reconstruction problem can be formulated as a sinogram recovery problem. The sinogram recovery problem is to find a complete sinogram based on the measured incomplete sinogram. In this paper, we solve the sinogram recovery problem by using linear prediction techniques. Since the scattered field of a target can be written as a superposition of distinct specular reflections arising from scattering centers on the target, the trace of the scattering centers can be predicted using linear prediction with the change of the observation angle. Thus, the missing data may be predicted before reconstructing the image. Some useful results obtained using the proposed method are presented