A super-resolution reconstruction approach of radar image using an adaptive-threshold singular value decomposition (SVD) technique was presented,and its performance was analyzed,compared and assessed detailedly.First,...A super-resolution reconstruction approach of radar image using an adaptive-threshold singular value decomposition (SVD) technique was presented,and its performance was analyzed,compared and assessed detailedly.First,radar imaging model and super-resolution reconstruction mechanism were outlined.Then,the adaptive-threshold SVD super-resolution algorithm,and its two key aspects,namely the determination method of point spread function (PSF) matrix T and the selection scheme of singular value threshold,were presented.Finally,the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar (SAR) images,and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio (SNR).Five versions of SVD algorithms,namely 1) using all singular values,2) using the top 80% singular values,3) using the top 50% singular values,4) using the top 20% singular values and 5) using singular values s such that s2≥max(s2)/rinSNR were tested.The experimental results indicate that when the singular value threshold is set as smax/(rinSNR)1/2,the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.展开更多
This paper proposes an autofocus algorithm used for Synthetic Aperture Radar (SAR) images, called Adaptive Kurtosis Optimization Autofocus Algorithm (AKOAA). The AKOAA can reduce the differ-ence between initial value ...This paper proposes an autofocus algorithm used for Synthetic Aperture Radar (SAR) images, called Adaptive Kurtosis Optimization Autofocus Algorithm (AKOAA). The AKOAA can reduce the differ-ence between initial value and real value in focusing by adaptively adjusting the initial value, therefore effec-tively improve the local extremum problem in the Contrast Optimization Autofocus Algorithm (COAA) and speed up the convergence velocity. The principle and realization method of AKOAA are thoroughly investi-gated, and experimental results using real L-band SAR data show that the focus speed of AKOAA is nearly doubled compared with that of the COAA, and the image contrast of AKOAA is improved as well.展开更多
针对超声相控阵全聚焦成像的成像分辨率不高且成像耗时长的缺点,从波束合成技术入手,引入合成发射孔径技术,提高成像横向分辨率。基于有效孔径和点散射函数设计稀疏阵列,从而减少成像数据量,并加入自适应加权函数,得到具有全孔径效应的...针对超声相控阵全聚焦成像的成像分辨率不高且成像耗时长的缺点,从波束合成技术入手,引入合成发射孔径技术,提高成像横向分辨率。基于有效孔径和点散射函数设计稀疏阵列,从而减少成像数据量,并加入自适应加权函数,得到具有全孔径效应的稀疏阵列,建立基于自适应加权函数的相控阵稀疏全聚焦成像算法模型,并在实验仿真平台Matlab Field Ⅱ上进行验证。结果表明,该算法有效提高了超声相控阵全聚焦成像分辨率,达到在保持成像分辨率的同时加快成像速度的目的。展开更多
毫米波合成孔径雷达(Ka-SAR)进行俯仰向数字波束形成(digital beam forming,DBF)车载地面验证时,由于车载高程较小使得成像区域地形起伏不可忽略。采用传统扫描接收(scan on receive,SCORE)算法获得的DBF加权系数会存在误差,使合成波束...毫米波合成孔径雷达(Ka-SAR)进行俯仰向数字波束形成(digital beam forming,DBF)车载地面验证时,由于车载高程较小使得成像区域地形起伏不可忽略。采用传统扫描接收(scan on receive,SCORE)算法获得的DBF加权系数会存在误差,使合成波束方向图偏离理想状态,降低系统性能。针对上述问题,本文提出了一种基于多通道SAR的自适应距离向DBF处理算法,对多通道数据进行干涉处理,并通过滤波提取干涉相位,自适应生成加权系数,提高了接收增益。该自适应算法获得的加权系数精度较高,具有处理流程简单、运算量小、便于实时处理的特点。最后,基于仿真和车载实验数据成像,验证了该算法的有效性。展开更多
基金Project(2008041001) supported by the Academician Foundation of China Project(N0601-041) supported by the General Armament Department Science Foundation of China
文摘A super-resolution reconstruction approach of radar image using an adaptive-threshold singular value decomposition (SVD) technique was presented,and its performance was analyzed,compared and assessed detailedly.First,radar imaging model and super-resolution reconstruction mechanism were outlined.Then,the adaptive-threshold SVD super-resolution algorithm,and its two key aspects,namely the determination method of point spread function (PSF) matrix T and the selection scheme of singular value threshold,were presented.Finally,the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar (SAR) images,and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio (SNR).Five versions of SVD algorithms,namely 1) using all singular values,2) using the top 80% singular values,3) using the top 50% singular values,4) using the top 20% singular values and 5) using singular values s such that s2≥max(s2)/rinSNR were tested.The experimental results indicate that when the singular value threshold is set as smax/(rinSNR)1/2,the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.
文摘This paper proposes an autofocus algorithm used for Synthetic Aperture Radar (SAR) images, called Adaptive Kurtosis Optimization Autofocus Algorithm (AKOAA). The AKOAA can reduce the differ-ence between initial value and real value in focusing by adaptively adjusting the initial value, therefore effec-tively improve the local extremum problem in the Contrast Optimization Autofocus Algorithm (COAA) and speed up the convergence velocity. The principle and realization method of AKOAA are thoroughly investi-gated, and experimental results using real L-band SAR data show that the focus speed of AKOAA is nearly doubled compared with that of the COAA, and the image contrast of AKOAA is improved as well.
文摘基于目标阴影的跟踪技术是视频合成孔径雷达(video synthetic aperture radar,ViSAR)目标探测的重要手段,但ViSAR数据存在目标特征不明显且随时间不规则变化、相干斑噪声干扰强等问题,使得ViSAR目标阴影跟踪精度较低。为此,提出了一种鲁棒的基于时间信息加权的ViSAR目标阴影跟踪算法。针对目标特征不明显且随时间不规则变化的问题,将尺度自适应均值偏移(adaptive scale mean shift,ASMS)跟踪算法引入到ViSAR目标阴影跟踪中,同时在ASMS算法的背景比例加权(background ratio weighted,BRW)技术中添加历史帧的特征,并对尺度正则项进行时间信息加权修正,来对目标特征进行整合。针对相干斑噪声干扰强的问题,对ASMS算法加入局部中值滤波操作的预处理步骤,在不增加计算量的同时平滑了噪声。在对两类ViSAR数据集上不同尺寸、不同运动状态的目标阴影的跟踪实验结果表明,与现有高性能跟踪算法相比,所提算法在保证了实时性的基础上提高了跟踪精度,且不需要额外的训练样本,具备较好的工程应用价值。
文摘针对超声相控阵全聚焦成像的成像分辨率不高且成像耗时长的缺点,从波束合成技术入手,引入合成发射孔径技术,提高成像横向分辨率。基于有效孔径和点散射函数设计稀疏阵列,从而减少成像数据量,并加入自适应加权函数,得到具有全孔径效应的稀疏阵列,建立基于自适应加权函数的相控阵稀疏全聚焦成像算法模型,并在实验仿真平台Matlab Field Ⅱ上进行验证。结果表明,该算法有效提高了超声相控阵全聚焦成像分辨率,达到在保持成像分辨率的同时加快成像速度的目的。
文摘毫米波合成孔径雷达(Ka-SAR)进行俯仰向数字波束形成(digital beam forming,DBF)车载地面验证时,由于车载高程较小使得成像区域地形起伏不可忽略。采用传统扫描接收(scan on receive,SCORE)算法获得的DBF加权系数会存在误差,使合成波束方向图偏离理想状态,降低系统性能。针对上述问题,本文提出了一种基于多通道SAR的自适应距离向DBF处理算法,对多通道数据进行干涉处理,并通过滤波提取干涉相位,自适应生成加权系数,提高了接收增益。该自适应算法获得的加权系数精度较高,具有处理流程简单、运算量小、便于实时处理的特点。最后,基于仿真和车载实验数据成像,验证了该算法的有效性。
