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主题 : 2009年全国优秀博士论文:超宽带SAR浅埋目标成像与检测的理论和技术研究
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楼主  发表于: 2009-10-10   

2009年全国优秀博士论文:超宽带SAR浅埋目标成像与检测的理论和技术研究

论文题目:超宽带SAR浅埋目标成像与检测的理论和技术研究 5>Z):*S  
  作者简介:金添,男,1980年2月出生,2004年2月师从于国防科学技术大学周智敏教授,于2007年6月获博士学位。 i8~C5Lt  
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  中文摘要 VHKkd;&KO)  
  在世界各国遗留的大约1.1亿颗地雷严重威胁了人民生命安全,阻碍了当地经济发展。而且每清除一颗30美元的地雷,需要花费300到1000美元。目前这些遗留地雷以现在的投资与技术需要1400年才能清除完毕,因此迫切需要一种高效的探雷手段。机载或车载超宽带合成孔径雷达(SAR)能够实现大区域浅埋目标的快速探测,克服了传统探雷手段效率低、安全性差的缺点,成为了探雷技术新的发展方向。本课题在国防预研项目和武器装备演示验证项目的支持下,深入研究了超宽带SAR成像与检测的基本理论,提出了浅埋目标成像与检测一体化流程和基于时频表示的实现方法,并在成像与检测一体化框架下,对浅埋目标折射和色散补偿、射频干扰抑制、相干斑噪声抑制、浅埋目标特征提取和鉴别器设计等方面进行了有意义的探索,得到了有效的解决方法,成功应用于后续装备型号的研制。本文的主要工作和创新点有: i_[_ye  
  1、深入研究了超宽带SAR传统成像与检测流程存在的问题,结合超宽带SAR大相对带宽和大波束角特性,提出了“成像与检测一体化框架”的思想和基于时频表示的实现方法。该框架充分挖掘超宽带SAR目标散射中的频率和方位角信息对处理性能的改善潜力,具体包括“面向检测的成像”和“基于成像的检测”两方面内容:“面向检测的成像”主要研究成像处理在获得高分辨SAR图像的同时,如何基于回波提取目标散射的频率和方位角信息,提高预筛选性能;而“基于成像的检测”主要针对预筛选获得的若干怀疑目标,研究如何基于图像提取目标散射的频率和方位角信息,有效剔除杂波从而提高鉴别性能。传统成像与检测流程忽视了成像与检测的有机联系,不能有效解决图像分辨率与频率和方位角信息提取精度之间的矛盾,限制了最终检测性能的提高。本文基于建立的超宽带SAR目标回波和成像模型,提出了一种面向检测的时频表示成像算法(TFRIF),该方法的图像域形式也可有效解决浅埋目标“基于成像的检测”这一复杂技术难题。基于TFRIF的成像与检测一体化框架实现方法,在几乎不损失分辨率的情况下,能够从回波或图像中精确获取目标散射的频率和方位角信息,既改善了预筛选和鉴别性能,又提高了成像与检测流程的整体处理效率。 I3 Y/^q  
  2、针对空气和土壤组成的多层传播媒质会引起浅埋目标回波畸变的问题,研究了浅埋目标成像中的折射和色散影响校正方法。在已知埋设深度、入射角等先验信息的条件下,提出了回波域折射和色散影响校正(EDRDC)的修正波前重构(MWR)和浅地表后向投影(SBP)两种浅埋目标成像算法。MWR和SBP算法与基于折射点求解的时域算法和基于相位迁移的频域算法等相比,不仅运算效率更高,而且考虑了土壤介电常数随频率的变化特性,聚焦性能和定位精度也更好。针对实际应用中目标埋设深度、入射角等先验信息无法获取等问题,提出了成像与检测一体化框架下的图像域折射和色散影响校正(IDRDC)浅埋目标聚焦和定位方法。IDRDC方法基于方位压缩增益最大准则估计埋设深度,并且校正因子不仅考虑了土壤介电常数随的频率特性,而且考虑了雷达不同方位位置对应的入射角不同,比EDRDC方法具有更好的聚焦性能和定位精度。IDRDC方法针对每个目标在图像域分别进行校正,适合解决不同埋设深度和土壤环境的多个浅埋目标的聚焦和定位问题,能够满足机载和车载超宽带SAR大面积区域探测的实际要求。 v .?8%[B  
  3、提出了实用的超宽带SAR射频干扰(RFI)和相干斑噪声抑制技术。在RFI抑制方面,针对超宽带SAR工作频段中的电视、广播和通讯等RFI信号严重影响成像质量的问题,提出了一种用于RFI抑制的Wiener滤波器构造新方法。由于RFI信号具有非平稳特性,传统方法需要实时录取RFI信号来构造Wiener滤波器。而新方法基于目标回波和射频信号二维频域支撑区的不同估计RFI频域特性,从而利用包含目标回波和RFI信号的雷达接收信号直接构造Wiener滤波器,在降低了系统复杂度的同时保证了良好的RFI抑制性能。在相干斑噪声抑制方面,针对车载前视超宽带SAR在行进过程中对前方区域连续成像,能够获得同一区域多幅不同俯视角图像的工作特点,采用多视处理抑制相干斑噪声。为了提高多视处理中不同俯视角图像的配准效率,提出了地距平面聚焦后向投影成像算法及其相应的折射和色散影响校正技术。不同俯视角地距平面图像之间只存在平移,克服了传统斜距平面成像结果之间的畸变具有空变特性,配准操作复杂的问题。 @0 31WAG"  
  4、针对金属地雷和未爆物两种典型浅埋目标,研究了成像与检测一体化框架下的浅埋目标特征提取技术。对于金属地雷目标,首先利用物理光学法建立了浅埋金属地雷电磁模型,定量分析了金属地雷双峰特征,提出了基于图像域的金属地雷双峰特征增强算法;在此基础上,提出了基于空间-波数分布(SWD)的金属地雷斜距-方位-频率-方位角四维散射函数估计及其特征选择方法,提取了包含双峰特性及方位不变性的特征向量。对于未爆物目标,首先利用SWD得到未爆物斜距-方位-频率-方位角四维散射函数估计,然后在提取了不同频率下的多方位特征幅度信息的基础上,利用Hu不变矩进一步定量描述未爆物不同频率下的多方位特征空间分布信息。成像与检测一体化框架下的金属地雷和未爆物特征提取方法与传统成像与检测流程常用的子带-子孔径技术相比,在获得频率和方位角信息的同时,保持了高分辨率,能够获得更有效的特征向量。 :'G\0U  
  5、提出了模糊超球面支持向量机(FHS-SVM)浅埋目标鉴别算法,并对FHS-SVM的超参数优化和核函数选择两个问题进行了深入研究。浅埋目标鉴别具有训练样本少、无典型杂波样本、浅埋目标与杂波误判风险不同以及埋设环境多样性等特点。根据浅埋目标鉴别的特点,对在许多领域的分类问题中取得迄今为止最好分类结果的超平面SVM进行了改进,得到利用核特征空间的超球面区分浅埋目标和杂波的FHS-SVM。FHS-SVM基于结构风险最小原理,在有效解决小样本学习问题的同时,只需要浅埋目标训练样本就能优化超球面参数,获得较好的浅埋目标和杂波分类性能;并且利用训练样本的隶属度将误判风险和埋设环境多样性等因素融入鉴别器学习过程,提高了FHS-SVM浅埋目标鉴别算法的实用性。在超参数优化方面,证明了FHS-SVM与第一层贝叶斯推理的等价性,提出了基于证据框架的高斯核FHS-SVM超参数优化方法,有效降低了检测结果的总体误判风险,提高了金属地雷和未爆物的鉴别性能。基于证据框架的超参数优化方法在保证优化性能的同时,克服了边缘分布分析和理论误差上限逼近等方法采用穷举搜索最优超参数,计算效率不高的问题。在核函数选择方面,提出用描述未爆物多方位特性的隐马尔可夫模型核替换高斯核函数,进一步改善了FHS-SVM对未爆物的鉴别性能。利用隐马尔可夫模型核FHS-SVM进行未爆物鉴别,将未爆物散射的多方位特征结合到鉴别器设计中,充分体现了“基于成像的检测”利用目标散射的频率和方位角信息提高鉴别性能的思想。 :[=bh4r~!  
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  关键词:  超宽带,合成孔径雷达,地表穿透,浅地表成像,浅埋目标检测,特征提取,鉴别器设计 5&/(!NGt  
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沙发  发表于: 2009-10-10   
Research on Theory and Technique of Ultra-wideband SAR Shallow Buried Targets Imaging and Detection l*r#  
Jin Tian &F0yur:Fz  
ABSTRACT me.n6Hd w  
There are up to 110 million landmines in many countries all over the world, which thread people’s safe and obstruct economic development seriously. In addition, it will cost 300 to 1000 dollars to eliminate a 30-dollar landmine. At the current investment and technology, it will take about 1400 years to remove all landmines. Therefore, an efficient landmine detection instrument is urgently needed. Air- or vehicle-borne ultra-wideband synthetic aperture radar (SAR) can perform quick detection of shallow buried targets over large areas, which overcomes the low-efficiency and low-safety shortcomings of those traditional landmine detection measurements, and has became a new tread of landmine detection technique. With the support of the defense pre-research project and the weapon equipment demonstration project, this dissertation has comprehensively studied the basic theory of ultra-wideband SAR imaging and detection and proposed the imaging and detection integrated procedure for shallow buried targets and the time-frequency representation based realization method. In the imaging and detection integrated framework, shallow buried object refraction and dispersion compensation, radio frequency interference (RFI) suppression, speckle noise suppression, shallow target feature extraction and discriminator design have been investigated and some useful solutions have been obtained, which place a great role in the following equipment research project. The major work and innovations are introduced as follows: *Z;!sqYmi  
1) The problems in the traditional ultra-wideband SAR imaging and detection procedure have been comprehensively studied. Based on the large relative bandwidth and wide beamwidth characteristic of ultra-wideband SAR, the “imaging and detection integrated framework” concept and its time-frequency representation based realization method are proposed. The framework employs the frequency and aspect angle information of ultra-wideband SAR target scattering to improve the processing performance, which includes two parts: “detection oriented imaging” and “imaging based detection”. The “detection oriented imaging” mainly focuses on how to extract the frequency and aspect angle information of target scattering from the received echo to improve prescreening performance when obtaining high-resolution SAR images; The “imaging based detection”, operating on the extracted suspected targets by prescreening, mainly focuses on how to extraction the frequency and aspect angle information of target scattering from formed images to eliminate clutter and improve discrimination performance. The relationship of imaging and detection is often neglected in the traditional imaging and detection procedure and thus cannot solve the contradiction between the image resolution and the frequency and aspect angle information extraction precision, which limits the improvement of the final detection performance. In this dissertation, based on the developed target echo and imaging models of ultra-wideband SAR, a detection oriented time-frequency representation image formation (TFRIF) is proposed, and its image domain form can also solve the problem of “imaging based detection” for shallow buried targets. The TFRIF based imaging and detection integrated realization method can extraction the frequency and aspect angle information of target scattering accurately without sacrifice of resolution, which not only improves the prescreening and discrimination performance but also improve the whole processing efficiency of the imaging and detection procedure. ob&b3<&`G  
2) Considering the problem of shallow buried object echo distortion caused by the multi-layered propagation media of air and soil, the refraction and dispersion effects compensation for shallow buried object imaging is studied. With the priori-knowledge of buried depth, incident angle, and so on, two shallow buried image formation with the echo domain refraction and dispersion compensation (EDRDC), modified wavefront reconstruction (MWR) and subsurface back-projection (SBP), are proposed. Compared with the refraction point calculation based time domain algorithms and the phase shift based frequency domain algorithms, MWR and SBP not only have better computational efficiency but also consider the frequency varying characteristic of the soil relative permittivity to yield better focusing and locating performance. But the priori-knowledge of buried depth, incident angle, and so on cannot be obtained in practical applications, and thus the shallow buried object focusing and locating method with the image domain refraction and dispersion compensation (IDRDC) in the imaging and detection framework is proposed. The IDRDC method estimates the buried depth on the maximum azimuth compression amplification criterion and its compensation factor considers not only the soil dispersion characteristic but also the incident angle varying with radar at different azimuth positions. Therefore, the IDRDC method has better focusing and locating performance than the EDRDC method. The IDRDC method operates on each object in the image domain and thus can focus and locate multiple shallow buried objects in different buried depths and soil environments, which fulfill the practical requirement of large areas detection for air- and vehicle-borne ultra-wideband SAR. |C SG  
3) The practical RFI and speckle noise suppression techniques for ultra-wideband SAR are proposed. In the aspect of RFI suppression, considering the problem of TV, broadcast and communication signal in the ultra-wideband SAR operation frequency degrading image quality, a novel Wiener filter construction method for RFI suppression is proposed. Because RFI signals have the non-stationarity characteristic, the traditional Wiener filter construction method need record real-time RFI. The novel method is based on the region of support difference of target echo and RFI in the two-dimensional frequency domain to estimate the RFI frequency spectrum, and thus can employ the radar received signal, including target echo and RFI signal, to construct the Wiener filter, directly, which reduces the system complexity and ensures the good RFI suppression performance. In the aspect of speckle noise suppression, considering the operation characteristic of vehicle-borne forward-looking ultra-wideband SAR that it continuously obtain several images of different depression angles on the same area when moving ahead, the multi-look technique is adopted to suppression speckle noise. In order to improve the registration efficiency of the several images of different depression angles in the multi-look processing, the ground-plane focusing back-projector image formation and its associated refraction and dispersion compensation technique are proposed. The images in the slant-plane have spatial varying distortion and thus need complex registration operation, but only shift exists among those ground-plane images of different depression angles.   %9|Z=\Y%  
4) On the two typical shallow buried targets, metallic landmine and unexploded ordnance, the shallow buried target feature extraction technique is studied in the imaging and detection integrated framework. For metallic landmines, the electromagnetic model of a shallow buried metallic landmine is built via the physical optics method to analyze the double-peak feature of the metallic landmine quantitatively and then the metallic landmine double-peak feature enhancement algorithm in the image domain is proposed. Furthermore, the space-wavenumber distribution (SWD) based method to estimate the four dimensional metallic landmine scattering function of slant range, azimuth, frequency and aspect angle and its associated feature selection method are proposed, which obtain the feature vector with the double-peak and the aspect-invariance features. For unexploded ordnances, the four dimensional unexploded ordnance scattering function of slant range, azimuth, frequency and aspect angle is firstly estimated via the SWD. Secondly, the amplitude information of the multi-aspect feature in different frequencies is extracted, and the spatial distribution information of multi-aspect feature in different frequencies is quantitative described using the Hu moment invariants further. Compared with the subband-subaperture technique used in the traditional imaging and detection procedure, the metallic landmine and unexploded ordnance feature extraction methods in the imaging and detection integrated framework can obtain the frequency and aspect angle information while maintain high resolution, and thus can obtain more efficient feature vectors. }$FvIG   
5) The fuzzy hypersphere support vector machine (FHS-SVM) is proposed for shallow buried target discrimination, and its two aspects, hyperparameter optimization and kernel function selection, are comprehensively studied. Shallow buried target discrimination have several characteristics: a small training sample set, without typical clutter samples, different misclassification risks for shallow buried target and clutter, and buried environment diversity. According to the above characteristics of shallow buried target discrimination, the hyperplane SVM, which has achieved the best classification results for many classification problems in different fields by now, has been modified to yield the FHS-SVM, which uses the hypersphere in the kernel space to separate shallow buried target and clutter. The FHS-SVM based on the structural risk minimization criterion can solve the small sample learning problem, which can get a good shallow buried target and clutter classification performance with only shallow buried target training samples to obtain the parameters of hypersphere. Furthermore, the factors of misclassification risk and bury environment diversity are combined into the discriminator study procedure using the fuzzy membership of training samples, which improve the practical value of the FHS-SVM in shallow buried target discrimination. In the aspect of hyperparameter optimization, the equality between the FHS-SVM and the first level Bayesian inference is proved, and the evidence framework based hyperparameter optimization method for the Gaussian kernel FHS-SVM is proposed, which reduces the total misclassification risk of the detection result and improve the metallic landmine and unexploded ordnance discrimination performance. The evidence framework based hyperparameter optimization method can ensure good optimization performance and has better computational efficiency than those exhaustive search methods such as the margin distribution analysis, error upper bound approximation, and so on. In the aspect of the kernel function selection, the Gaussian kernel is replaced by the hidden Markov model kernel, which describes the multi-aspect characteristic of unexploded ordnance, to improve the FHS-SVM unexploded ordnance discrimination performance further. The employment of hidden Markov model kernel FHS-SVM to the unexploded ordnance discrimination combines the multi-aspect characteristic of unexploded ordnance scattering into the discriminator design, which shows the thought of “imaging based detection” that use the frequency and aspect angle information of target scattering to improve the discrimination performance. 'ZJ`qKQ  
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Key words:  ultra-wideband, synthetic aperture radar, ground penetrating, subsurface imaging, shallow buried target detection, feature extraction, discriminator design
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板凳  发表于: 2011-08-12   
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