时域压缩合成孔径超分辨水声成像算法

提出一种针对水下稀疏目标的时域压缩合成孔径声呐成像方法(TC-SAS),实现了水声目标高分辨实时成像。通过多子阵的孔径合成,在时域上构造出成像网格格点到有效孔径内逐帧阵列的格林函数,并给出成像区域散射强度到数据域的映射矩阵;然后利用该区域空域稀疏的先验知识,通过正交匹配追踪的稀疏重构方式,解算出成像区域散射系数矩阵,实现了稀疏目标高分辨成像.同时,针对线性调频信号提出数据缩减的方法,通过对观测数据和字典矩阵同时脉压后截取,减小了数据规模;进一步结合二维矩阵数表查表的方法,以空间换时间,实现了区块实时成像。数值仿真以及湖试试验表明,所提算法能分辨出传统的时延求和算法难以分辨的目标,并且在图像清晰度指标上平均提升4.9 dB.改善了合成孔径声呐的成像质量.      

压缩感知在合成发射孔径医学超声成像中的应用

为了解决合成发射孔径技术在医学超声成像实现中面临的数据量大及接收通道多的问题,提出一种超声成像系统频率域稀疏性模型的压缩感知成像算法。首先对超声系统频率域稀疏性模型进行了验证;然后根据稀疏性模型利用压缩感知理论对回波信号进行压缩采样,并使用最优化方法完成回波信号重建;最终通过合成发射孔径技术完成超声成像。针对医学成像中常用的点目标及模拟胎儿目标进行成像仿真实验,对重建图像在均方误差、分辨率及成像质量等方面与常规成像结果对比分析。实验结果表明在保证成像质量的同时,仅使用30%原始数据量及50%总接收通道数目可完成成像;频率域稀疏性模型的压缩感知成像算法可以大幅度减少合成发射孔径成像所需数据量及接收通道数,极大地降低了系统复杂度。      

一种强噪声背景下微弱超声信号提取方法研究

为解决在强噪声背景下获取超声信号的难题,基于粒子群优化算法和稀疏分解理论提出一种强噪声背景下微弱超声信号提取方法.该方法将降噪问题转换为在无穷大参数集上对函数进行优化的问题,首先以稀疏分解理论和超声信号的结构特点为依据构建了粒子群优化算法运行所需要的目标函数及去噪后信号的重构函数,从而将粒子群优化算法和超声信号降噪联系在一起;然后根据粒子群优化算法可以在连续参数空间寻优的特点建立了用于匹配超声信号的连续超完备字典,并采用改进的自适应粒子群优化算法在该字典中对目标函数进行优化;最后根据对目标函数在字典上的优化结果确定最优原子,并利用最优原子按照重构函数重构出降噪后的超声信号.通过对仿真超声信号和实测超声信号的处理,结果表明本文提出的方法可以有效提取信噪比低至-4 dB的强噪声背景下的微弱超声信号,且和基于自适应阈值的小波方法相比本文方法表现出更好的降噪性能.     

水声信号稀疏重构高分辨角度-多普勒成像

针对傅氏空时二维谱估计分辨率低以及声呐空时采样数据样本数不足给角度-多普勒成像带来困难的问题,提出一种水声信号稀疏重构的高分辨角度-多普勒成像方法和抗混响空时滤波器的稀疏重构方法。该方法在声呐阵列单测量向量的极少观测样本条件下,建立阵列信号的空时稀疏表示模型,应用稀疏表示的匹配追踪算法和基追踪算法重构回波与混响的高分辨角度-多普勒像。并根据运动声呐回波与混响的空时分布规律及声呐待检测距离单元位置的先验信息,沿着混响空时分布脊线设计混响稀疏表示的专用空时导向向量字典,通过重构抗混响空时滤波器来抑制角度-多普勒平面的混响干扰。对运动声呐前视和侧视阵列的计算机仿真结果表明,在混响背景中,该方法采用声呐阵列单测量向量重构了低速运动目标多亮点回波的高分辨角度-多普勒像,频率分辨率突破傅里叶分辨率,角度分辨率突破阵列瑞利限,分辨率明显优于傅氏空时谱估计。      

超声断层扫描数据压缩的时间窗构造及数据提取方法

介绍了一种针对三维超声断层扫描系统的数据压缩方法,该方法通过检测最早的目标反射重构目标表面位置;依据表面位置,计算目标回波信号的起止时间;以此为依据构造时间窗,标明成像算法所需数据在采样序列中存在的时间范围;最终提取有用数据,完成数据压缩。临床数据被用于算法的效果评价。结果表明,该方法可达到平均2.27的压缩率和0.21的压缩率标准差,以及2.25的数据传输加速比。较传统数据压缩方法,该方法只利用回波时域信息,可作为预压缩方法和传统方法结合使用以获得更高的压缩率。      

逆合成孔径成像激光雷达数据采样技术

逆合成孔径成像激光雷达能够实现对运动目标的高分辨实时成像,但激光信号的极大带宽和目标回波信号的微弱性给雷达回波数据的接收和处理带来了较大困难.针对这一问题,提出了基于光外差探测手段和压缩感知理论相结合的信号采样方法,首先通过光外差探测降低回波信号的有效带宽,再结合压缩感知理论实现对信号的稀疏化采样和重构.仿真结果证明了运用本文所提出的采样方法,在使用远低于奈奎斯特定理所规定的采样率时,仍然能够实现对目标的高质量成像.     

逆合成孔径成像激光雷达数据采样技术

逆合成孔径成像激光雷达能够实现对运动目标的高分辨实时成像,但激光信号的极大带宽和目标回波信号的微弱性给雷达回波数据的接收和处理带来了较大困难.针对这一问题,提出了基于光外差探测手段和压缩感知理论相结合的信号采样方法,首先通过光外差探测降低回波信号的有效带宽,再结合压缩感知理论实现对信号的稀疏化采样和重构.仿真结果证明了运用本文所提出的采样方法,在使用远低于奈奎斯特定理所规定的采样率时,仍然能够实现对目标的高质量成像.     

脉冲压缩与互相关联合的合成孔径声呐回波时延补偿

针对合成孔径声呐中阵元相位不一致导致互相关时延补偿算法对成像质量提升效果有限的问题,提出了一种脉冲压缩与互相关联合的回波时延补偿算法.该算法利用脉冲压缩回波的互相关对原始回波相位畸变进行校正,实现粗补偿;联合脉冲压缩与偏移相位中心算法实现精细时延补偿,对不同合成孔径位置各阵元回波时延差实现了较为准确的估计,增强了成像效果。试验数据经该算法处理后,回波时延得到较为精确的补偿,地貌成像结果的亮度、对比度等统计特性得到不同程度的提高,且纹理细节增多;典型线缆目标的成像聚焦加深,成像长度误差约由5%减小为0.8%。试验结果显示,该算法对互相关时延补偿方法改进效果明显,验证了算法的可行性、有效性。      

逆合成孔径成像激光雷达实包络成像算法

逆合成孔径激光成像雷达受激光调制技术以及回波相位信息易受大气湍流破坏的限制,采用常规的相位相干积累类方法得到目标二维高分辨图像很困难.针对这一情况,提出了一种基于逆Radon变换的实包络成像算法.利用回波距离脉冲压缩后的实包络信息,实现方位向的非相干积累,最终得到二维高分辨图像.通过该算法,成像系统可以使用非相干激光信号,在脉冲重复频率较低且存在大气湍流的情况下,也可以获得高质量的成像结果.仿真实验验证了此算法的有效性和优越性.     

逆合成孔径成像激光雷达实包络成像算法

逆合成孔径激光成像雷达受激光调制技术以及回波相位信息易受大气湍流破坏的限制,采用常规的相位相干积累类方法得到目标二维高分辨图像很困难.针对这一情况,提出了一种基于逆Radon变换的实包络成像算法.利用回波距离脉冲压缩后的实包络信息,实现方位向的非相干积累,最终得到二维高分辨图像.通过该算法,成像系统可以使用非相干激光信号,在脉冲重复频率较低且存在大气湍流的情况下,也可以获得高质量的成像结果.仿真实验验证了此算法的有效性和优越性.     

Effect of sparse basis selection on ultrasonic signal representation

Recently, adaptive sparse representations of ultrasonic signals have been utilized to improve the performance of scanning acoustic microscopy (SAM), a common nondestructive tool for failure analysis of microelectronic packages. The adaptive sparse representation of an ultrasonic signal is generated by decomposing it in a learned overcomplete dictionary using a sparse basis selection algorithm. Detection and location of ultrasonic echoes are then performed on the basis of the resulting redundant representation. This paper investigates the effect of sparse basis selection algorithms on ultrasonic signal representation. The overcomplete independent component analysis, focal underdetermined system solver (FOCUSS), and sparse Bayesian learning algorithms are examined. Numerical simulations are performed to quantitatively analyze the efficiency of ultrasonic signal representations. Experiments with ultrasonic A-scans acquired from flip-chip packages are also carried out in the study. The efficiency of ultrasonic signal representations are evaluated in terms of the different criteria that can be used to measure its performance for different SAM applications, such as waveform estimation, echo detection, echo location and C-scan imaging. The results show that the FOCUSS algorithm performs best overall.     

Sparse signal representation and its applications in ultrasonic NDE

Many sparse signal representation (SSR) algorithms have been developed in the past decade. The advantages of SSR such as compact representations and super resolution lead to the state of the art performance of SSR for processing ultrasonic non-destructive evaluation (NDE) signals. Choosing a suitable SSR algorithm and designing an appropriate overcomplete dictionary is a key for success. After a brief review of sparse signal representation methods and the design of overcomplete dictionaries, this paper addresses the recent accomplishments of SSR for processing ultrasonic NDE signals. The advantages and limitations of SSR algorithms and various overcomplete dictionaries widely-used in ultrasonic NDE applications are explored in depth. Their performance improvement compared to conventional signal processing methods in many applications such as ultrasonic flaw detection and noise suppression, echo separation and echo estimation, and ultrasonic imaging is investigated. The challenging issues met in practical ultrasonic NDE applications for example the design of a good dictionary are discussed. Representative experimental results are presented for demonstration.     

Undersampled MRI reconstruction with patch-based directional wavelets

Compressed sensing has shown great potential in reducing data acquisition time in magnetic resonance imaging (MRI). In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a preconstructed basis or dictionary. In this paper, patch-based directional wavelets are proposed to reconstruct images from undersampled k-space data. A parameter of patch-based directional wavelets, indicating the geometric direction of each patch, is trained from the reconstructed image using conventional compressed sensing MRI methods and incorporated into the sparsifying transform to provide the sparse representation for the image to be reconstructed. A reconstruction formulation is proposed and solved via an efficient alternating direction algorithm. Simulation results on phantom and in vivo data indicate that the proposed method outperforms conventional compressed sensing MRI methods in preserving the edges and suppressing the noise. Besides, the proposed method is not sensitive to the initial image when training directions.     

分块稀疏信号1-bit压缩感知重建方法

1-bit压缩感知理论指出:对稀疏信号进行少量线性投影并对投影信号进行1-bit量化,该1-bit信号包含足够的信息,从而能对原始信号进行高精度重建.然而,当信号难以进行稀疏表达时,传统1-bit压缩感知算法无法精确重建原始信号.前期研究表明,分块稀疏模型作为一种特殊的结构型稀疏模型,对于难以用传统稀疏模型进行表达的信号具有较好的表达作用.本文提出了一种针对分块稀疏信号的1-bit压缩感知重建方法,该方法利用分块稀疏的统计特性对信号进行数学建模,通过变分贝叶斯推断方法进行信号重建并在光电容积脉搏波(photoplethysmography)信号上进行了实验验证.实验结果表明,与现有1-bit压缩感知重建方法相比,本文方法重建精度更高,且收敛速度更快.     

稀疏阈值的超分辨率图像重建

为了解决基于字典学习的超分辨重构算法耗时过长的问题,提出了基于稀疏阈值模型的图像超分辨率重建方法。首先,将联合字典理论与图像块稀疏阈值方法相结合,训练得到高、低分辨率过完备图像字典对。接着,通过稀疏阈值OMP算法对图像特征块进行稀疏表示。然后,通过高分辨率字典重构出初始的超分辨图像。最后,通过改进迭代反投影算法对初始的超分辨图像进行全局优化,从而进一步提高图像重构质量。实验结果表明,超分辨图像重构平均峰值信噪比(PSNR)为30.1 d B,平均结构自相似度(SSIM)为0.937 9,平均计算时间为10.2 s。有效提高了超分辨重构的速度,改善了重构高分辨图像的质量。     

A measurement-domain adaptive beamforming approach for ultrasound instrument based on distributed compressed sensing: Initial development

High efficient acquisition of the sensor array signals and accurate reconstruction of the backscattering medium are important issues in ultrasound imaging instrument. This paper presents a novel measurement-domain adaptive beamforming approach (MABF) based on distributed compressed sensing (DCS) which seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated with much few measurements under the Nyquist rate. Instead of sampling conventional backscattering signals at the Nyquist rate, few linear projections of the returned signal with random vectors are taken as measurements, which can reduce the amount of samples per channel greatly and makes the real-time transmission of sensor array data possible. Then high resolution ultrasound image is reconstructed from the few measurements of DCS directly by the proposed MABF algorithm without recovering the raw sensor signals with complex convex optimization algorithm. The simulated results show the effectiveness of the proposed method.     

近似零伪范数约束的水声信号压缩与重构

提出近似零伪范数约束的稀疏压缩与重构方法。该方法首先采用稀疏二进制矩阵作为测量矩阵,对信号进行压缩和传输;在接收端仅给定测量矩阵和压缩信号的条件下,采用小波滤波器设计字典,利用最陡梯度法寻优和投影方法求得信号的稀疏表达,最终结合稀疏表达值与字典用于水声数据重建,海试实验结合扫频以及单载频信号进行处理,采用NMSE、SNR以及算法运行时间作为算法的评估指标,以验证本文方法相对于传统算法在恢复精度上的提高。      

Three-dimensional sound source localization using distributed microphone arrays

To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments,a sound source localization method was proposed.This method exploited the inherent spatial sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing(CS) theory.In this method two-step discrete cosine transform(DCT)-based feature extraction was utilized to cover both short-time and long-time properties of the signal and reduce the dimensions of the sparse model.Moreover,an online dictionary learning(DL) method was used to dynamically adjust the dictionary for matching the changes of audio signals,and then the sparse solution could better represent location estimations.In addition,we proposed an improved approximate l_0norm minimization algorithm to enhance reconstruction performance for sparse signals in low signal-noise ratio(SNR).The effectiveness of the proposed scheme is demonstrated by simulation results where the locations of multiple sources can be obtained in the noisy and reverberant conditions.     

联合稀疏表示在水下目标分类中的应用

水下目标分类识别的性能受所选特征的限制,多特征往往可以获得更加稳定的结果,针对这一问题,提出了一种基于联合稀疏表示模型的水下目标分类识别方法。首先对水下目标回波信号提取3种具有信息互补性与关联性的特征:中心矩特征、小波包能量谱特征、梅尔频率倒谱系数特征,然后应用加速近端梯度法对联合稀疏表示模型进行优化,求解得到最优联合稀疏系数,最后根据最小误差准则确定目标类别。在消声水池开展模拟实验,对6类目标进行分类识别,结果表明:与传统算法相比,提出的算法具有更高识别准确率,并且其执行效率较传统算法有很大提升。      

字典学习稀疏表示的高光谱图像异常检测

针对稀疏表示高光谱检测算法性能受背景字典影响较大的问题,充分利用高光谱图像空间信息和光谱主成分信息,提出了一种基于字典学习的稀疏表示异常检测算法。首先利用主成分分析提取高光谱数据的主特征,建立目标主成分空间,并证明了在主成分空间进行字典学习稀疏重构的可行性;然后在主成分空间内构造基于K-SVD算法的训练字典,改善了背景字典性能;采用正交匹配算法重构主成分分量,利用主成分分析反变换得到待检测像元重构光谱,增强了高光谱图像的局部异常特性;最后,基于重构误差异常特性实现高光谱图像异常检测。仿真结果证明了该方法的有效性。