Compressive matched-field processing

Source localization by matched-field processing (MFP) generally involves solving a number of computationally intensive partial differential equations. This paper introduces a technique that mitigates this computational workload by "compressing" these computations. Drawing on key concepts from the recently developed field of compressed sensing, it shows how a low-dimensional proxy for the Green's function can be constructed by backpropagating a small set of random receiver vectors. Then the source can be located by performing a number of "short" correlations between this proxy and the projection of the recorded acoustic data in the compressed space. Numerical experiments in a Pekeris ocean waveguide are presented that demonstrate that this compressed version of MFP is as effective as traditional MFP even when the compression is significant. The results are particularly promising in the broadband regime where using as few as two random backpropagations per frequency performs almost as well as the traditional broadband MFP but with the added benefit of generic applicability. That is, the computationally intensive backpropagations may be computed offline independently from the received signals, and may be reused to locate any source within the search grid area.     

Broadband underwater multi-source localization with a computationally efficient coherent OMP algorithm

In this paper, we propose a broadband coherent matched-field processing (MFP) algorithm to solve multi-source localization problems in shallow water scenarios. The proposed algorithm combines the matched-phase coherent processor with the sparse recovery technique from compressive sensing (CS) theory. A greedy sparse recovery algorithm is adopted to iteratively locate multiple sources using a matched-phase coherent processor. At each iteration of the greedy algorithm, the data is processed coherently using the phase descent search (PDS) algorithm, rather than the incoherent methods used in many sparse recovery algorithms, such as the classical orthogonal matching pursuit (OMP) algorithm. The phase shifts between different frequencies are estimated and compensated, such that the performance can be greatly enhanced. The proposed algorithm is applied to simulated data, synthesized data, and data collected in the SWellEx-96 shallow water experiment. The result provides sparse localization information that matches the ground truth source locations in the simulation and the source trajectory calculated from the Global Positioning System (GPS) information from the experiment.     

A Bayesian approach to matched field processing in uncertain ocean environments

An approach of Bayesian Matched Field Processing （MFP） was discussed in the uncertain ocean environment. In this approach, uncertainty knowledge is modeled and spatial and temporal data received by the array are fully used. Therefore, a mechanism for MFP is found, which well combines model-based and data-driven methods of uncertain field processing. By theoretical derivation, simulation analysis and the validation of the experimental array data at sea, we find that （1） the basic components of Bayesian matched field processors are the cor- responding sets of Bartlett matched field processor, MVDR （minimum variance distortionless response） matched field processor, etc.; （2） Bayesian MVDR/Bartlett MFP are the weighted sum of the MVDR/Bartlett MFP, where the weighted coefficients are the values of the a posteriori probability; （3） with the uncertain ocean environment, Bayesian MFP can more correctly locate the source than MVDR MFP or Bartlett MFP; （4） Bayesian MFP can better suppress sidelobes of the ambiguity surfaces.     

频域单快拍压缩感知目标方位估计和信号恢复方法

方位估计和信号恢复分别是水下目标定位、跟踪和目标识别的前提。提出了一种阵列频域单快拍压缩感知的水下目标方位估计和信号恢复方法。首先将阵列接收数据变换到频域,取频域单快拍数据作为压缩感知的测量值,然后根据频域快拍对应的频率、搜索方位和阵列流形构造过完备的阵列流形矩阵作为压缩感知的感知矩阵,最后通过基追踪算法估计搜索方位上目标信号和功率,实现DOA估计与信号恢复。宽带仿真实验数据验证结果表明,同等条件下完成同样的目标方位分辨,提出的方法比最小方差无失真响应方法要求的阵元数和快拍数较少,要求的信噪比更低,恢复的目标信号更加准确,波形相关系数达到89%以上。海上实验数据处理结果表明,目标检测能力优于最小方差无失真响应方法,证明该方法可以适用于实际声呐系统。      

Vector Green's function algorithm for radiative transfer in plane-parallel atmosphere

Green's function is a widely used approach for boundary value problems. In problems related to radiative transfer, Green's function has been found to be useful in land, ocean and atmosphere remote sensing. It is also a key element in higher order perturbation theory. This paper presents an explicit expression of the Green's function, in terms of the source and radiation field variables, for a plane-parallel atmosphere with either vacuum boundaries or a reflecting (BRDF) surface. Full polarization state is considered but the algorithm has been developed in such way that it can be easily reduced to solve scalar radiative transfer problems, which makes it possible to implement a single set of code for computing both the scalar and the vector Green's function.     

Direction of arrival estimation and signal recovery based on single snapshot compressed sensing in frequency domain

Direction of arrival(DOA) estimation and signal recovery is the base of the underwater target localization,tracking and recognition.Based on the compressed sensing theory,a method for DOA estimation and source signal recovery is proposed using the single snapshot processing of the received array signal in frequency domain.The received array signal are transformed to frequency domain,and the single snapshot data in frequency domain are regarded as the measured data of the compressed sensing.According to the frequency,searching orientation and array manifold,the overcomplete array manifold is constructed as the sensing matrix of the compressed sensing.Both the target signal and power of the searching orientation are estimated by the basis pursuit method to complete DOA estimation and signal recovery.Simulation results show that the proposed method has a number of advantages over the minimum variance distortionless response(MVDR) method,including improved robustness to noise,fewer requirement in number of sensors and snapshots.And the correlation coefficient of the signal reaches up to 0.89.Experiment results in real environments verify that the proposed method performs more effectively in the detection of weak targets than the MVDR method and can be applied to real sonar system.     

双树小波变换与小波树稀疏联合的低场CS-MRI算法

压缩感知理论常用在磁共振快速成像上,仅采样少量的K空间数据即可重建出高质量的磁共振图像.压缩感知磁共振成像技术的原理是将磁共振图像重建问题建模成一个包含数据保真项、稀疏先验项和全变分项的线性组合最小化问题,显著减少磁共振扫描时间.稀疏表示是压缩感知理论的一个关键假设,重建结果很大程度上依赖于稀疏变换.本文将双树复小波变换和小波树稀疏联合作为压缩感知磁共振成像中的稀疏变换,提出了基于双树小波变换和小波树稀疏的压缩感知低场磁共振图像重建算法.实验表明,本文所提算法可以在某些磁共振图像客观评价指标中表现出一定的优势.     

空气中声源的水下匹配场定位

通过水下布放的垂直线列阵采集空中声源在水下激发的测量声场,采用声场波数积分模型(OASES模型)对空中声源激发的水下声场建模,计算出拷贝声场,将二者进行匹配处理从而对空中声源目标定位。首先通过数值仿真验证了匹配场处理技术对空中声源的测距能力,并通过引入宽带匹配场处理器平滑掉距离上的周期性旁瓣。最后分析南海某海域的空气声试验数据,采用常规匹配场方法对700 m以内的32组空中声源目标进行定位,测距结果与GPS计算的收发间实际距离相比,大多数情况下是一致的,在较远距离由于信噪比降低,测量结果容易出现偏差。      

Unified Green's function retrieval by cross correlation

It has been shown by many authors that the cross correlation of two recordings of a diffuse wave field at different receivers yields the Green's function between these receivers. Recently the theory has been extended for situations where time-reversal invariance does not hold (e.g., in attenuating media) and where source-receiver reciprocity breaks down (in moving fluids). Here we present a unified theory for Green's function retrieval that captures all these situations and, because of the unified form, readily extends to more complex situations, such as electrokinetic Green's function retrieval in poroelastic or piezoelectric media. The unified theory has a wide range of applications in "remote sensing without a source."     

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.     

The effects of the internal waves on temporal correlation of matched-field processing in shallow water

Effects of linear and solitary internal waves on the temporal correlation of matched-field processing(MFP) in shallow water are numerically investigated for acoustic sources with different frequencies and depths based on oceanographic data from an experiment.It is shown that the temporal correlation of MFP decreases as the amplitude of solitary internal waves or the average energy density of linear internal waves increases.For acoustic source with lower frequency or located below the thermocline,the temp...     

匹配场处理舰船辐射噪声级估计方法

针对水声信道对舰船辐射噪声声传播的影响,进而导致声源级测量结果不准确的问题,提出了基于匹配场处理的舰船辐射噪声级估计方法。在海洋环境噪声为空间均匀高斯白噪声的假设下,当海洋环境参数已知、信噪比满足一定要求时,匹配场处理能有效地给出被测噪声源的位置信息及该位置处的能量响应。从能量估计角度出发,推导了声源位置处匹配场输出响应的能量修正因子计算公式,从理论上证明了匹配场处理在被测声源位置处输出响应与能量修正因子的乘积为真实声源级的最小方差无偏(MVU)估计。该方法首先选择合适的声场计算模型计算拷贝场向量,对接收到的辐射噪声信号进行匹配场处理,得出接收信号级和被测声源位置;其次利用该位置所对应的拷贝场向量替换能量修正因子公式中的真实信道传输函数以计算能量修正因子的估计值;最后由接收信号级与能量修正因子估计值相乘得出舰船辐射噪声声源级的MVU估计。针对典型的浅海水声信道,进行了计算机仿真试验,结果表明:该方法能有效地进行舰船辐射噪声测量,当信噪比满足一定要求时,测量得到的声源级与实际声源级相比,误差小于1 dB。      

基于压缩感知估计的GIS击穿放电定位方法研究*

经典的空间谱估计方法,如多重信号分离(MUSIC)方法,对噪声敏感,难以在低信噪比环境中有效地进行波达方向估计。为提升在复杂电力环境中的气体绝缘金属封闭开关设备(GIS)击穿定位能力,该文提出了一种基于极化内插的压缩感知波达方向估计方法 CSP-DOA。该方法对传声器阵列接收到的数据进行建模,形成多测量矢量模型,结合压缩感知中稀疏重构技术进行波达方向估计;同时,该方法还采用极化内插技术解决了稀疏重构中的费网格问题,进一步提升了波达方向估计精度及计算效率。通过数值模拟对算法的定位效果进行了分析,仿真结果表明CSP-DOA方法对于击穿信号有更好的定位效果。结合可见光图像匹配实现了GIS击穿信源的二维可视化定位,在某高压大厅的GIS模型上进行耐压击穿定位试验研究,试验结果进一步验证了该文方法可较好的应用于GIS的击穿定位。     

基于压缩感知的反射光谱重构算法研究

光谱反射率描述物体的表面颜色特征,为了能够获取物体自身更加精确的颜色信息,在图像处理领域光谱反射率重构成为了关注的话题。反射光谱重构算法是对实验物体表面在可见光范围内每一波长处的光谱反射率进行重构,以达到提高物体自身颜色准确复制的精度,最后建立相应的反射光谱。尝试将压缩感知(CS)理论应用到光谱实验中,对光谱反射率进行重构。首先是介绍了压缩感知理论知识,然后把压缩感知理论与光谱反射率原理相结合,根据基于压缩感知的光谱反射率重构的理论框架,选取合适的采样值,压缩感知的采样值即压缩值,小波基作为正交矩阵,高斯随机矩阵作为测量矩阵,正交矩阵与测量矩阵需要保证具有不相关性,将原始光谱反射率从高维到低维进行线性投影,得到低维的观测信号,运行简单的正交匹配追踪算法(OMP)对低维的观测信号进行由低维到高维的高精度重构,重构得到的光谱反射率与原始光谱反射率具有相同的维度,最后将压缩感知重构算法与传统的光谱反射率重构算法伪逆法与多项式回归法进行比较。经过压缩感知重构算法得到的色差值与均方根误差值都小于伪逆法和多项式回归法重构的结果,经压缩感知的重构精度明显提高;经压缩感知重构的光谱曲线可以达到或者更接近原始光谱曲线的峰值,整体效果更接近原始光谱曲线;经多项式回归法和伪逆法重构的光谱曲线达不到原始峰值,整体上存在偏差。可以认为压缩感知用低采样的数据达到了全采样的效果,提高了光谱反射率重构的精度。基于压缩感知的光谱反射率重构算法效果明显优于传统的多项式回归法和伪逆法,可以将压缩感知理论应用到实际的多光谱成像系统中。     

Optical image encryption technique based on compressed sensing and Arnold transformation

A new optical image encryption method based on compressed sensing and Arnold transformation is proposed. First, dimensional reduction and random projection, the characteristics of compressed sensing, are utilized to compress and encrypt a digital image. Second, Arnold transformation is used to scramble the encryption image followed by compressed sensing with low data volume. Then, the encryption image is encrypted again by double random phase encoding optical encryption technique; two random phase masks generated by sequences of irrational number are been used as secret keys. In the end, the multi-encrypted information is embedded into the host image and transmitted. At the receiver, original image information is reconstructed approximately via orthogonal matching pursuit algorithm. The peak signal-to-noise ratio and the normalized cross-correlation between the original image and the decrypted one are used to calculate the quality of the decryption image. The experimental results demonstrate that our method is secure and robust.     

Matched localization of a horizontal array in shallow sea

I.IntroductionInsha1lowsea,passivesource1ocalizationisadifficu1t,yctinterestingprob1emthathasre-ceivcdagrcatdca1ofattcnti0ninthelastfCwycars.l'-'1Theconventiona1sourccloca1izationistOmatchthercceivcdacousticdatawiththesoundficldprcdictcdbythepropagationmodcl.Thistechniqueiscallcdmatchcd-ficldproccssing(MFP).Itiscommonlyacceptcdthatinshal-low-watercnvironmcnLssoundficldcanbcwclldcscribcdbyanormal-modemodel.Accord-ingtothismodcl,thcacousticprcssurecanbecxprcsscdasalincarcombinationofthenorma…     

基于压缩感知的振动数据修复方法

为解决旋转机械振动信号丢失数据修复的问题,提出一种基于压缩感知原理的振动数据修复方法.首先对采集到的不完整信号进行处理,将无信息输入时刻对应的数据用零元素填充得到有损信号,以单位矩阵为基础,根据有损信号中零元素的位置信息,构造对应于压缩感知框架下的观测矩阵;再根据待修复信号的特点并结合先验知识,构造或选择能够对振动信号进行稀疏表示的字典矩阵;然后使用高效且稳定的追踪算法,根据有损信号、观测矩阵以及字典矩阵重构原始信号,实现丢失振动数据的修复.使用仿真数据检验方法的有效性;使用实测的轴承振动状态数据验证方法对于振动数据的适用性,并通过比较完整信号、有损信号和修复信号对应的时域和频域特征值来检验数据修复效果.实验结果表明:本文方法能够有效地实现丢失数据的修复,且从统计特征的角度来看,相比于有损信号,修复信号能够更为准确地描述真实完整的振动信号.     

压缩感知在宽带声学多普勒测速技术中的应用*

为降低宽带声学多普勒测速技术中宽带回波信号处理系统的采样和数据存储压力,研究了压缩感知的回波信号重构算法,并将其应用于宽带声学多普勒测速的回波信号分析中。在点回波模型下进行宽带回波信号的仿真实验,利用复协方差法计算频移。仿真实验结果表明,在无噪声的理想条件下,利用压缩感知理论处理宽带多普勒测速的回波信号,能够达到理想的测频效果;在相同的噪声条件下,应用压缩感知方法处理后的回波信号能够获得与带通采样方法相当的测频效果。     

Quantitative single point imaging with compressed sensing

A novel approach with respect to single point imaging (SPI), compressed sensing, is presented here that is shown to significantly reduce the loss of accuracy of reconstructed images from under-sampled acquisition data. SPI complements compressed sensing extremely well as it allows unconstrained selection of sampling trajectories. Dynamic processes featuring short NMR signal can thus be more rapidly imaged, in our case the absorption of moisture by a cereal-based wafer material, with minimal loss of image quantification. The absolute moisture content distribution is recovered via a series of images acquired with variable phase encoding times allowing extrapolation to time zero for each image pixel and the effective removal of contrast.     

环境扰动约束下的稀疏贝叶斯定位方法

针对匹配场被动定位技术对海洋环境扰动敏感的问题,提出了一种基于稀疏贝叶斯学习的稳健匹配场处理方法。该方法通过分析环境扰动情况下的声场构成,建立了海洋环境扰动模型,同时结合水下定位问题的稀疏性,将匹配场被动定位问题表述为稀疏信号重构问题。然后,使用稀疏贝叶斯学习方法迭代更新目标位置及模型失配权向量,收敛至最优稀疏解作为目标定位结果。最后使用仿真数据和北厄尔巴岛的海试数据对算法进行了验证,仿真和实验结果表明:该算法在海洋环境模型失配情况下也能够准确定位,且能分辨水平间距为100 m的两个目标。因此,基于稀疏贝叶斯学习的稳健匹配场处理方法能够有效利用海洋环境扰动声场结构和水下定位稀疏特性,以增强匹配场处理的稳健性和定位精度,并且具有应对多源定位问题的能力。