随机多项式展开多特征向量约束-匹配场 声源定位算法*

多特征向量约束的自适应环境宽容匹配场算法可用于随机和不确知海洋声传播介质中的声源位置定位。该方法需要对拷贝场复声压的互谱矩阵进行估计。为了克服蒙特卡洛统计方法计算互谱矩阵较为耗时的缺点,文章将起伏介质中的随机或不确知声压表示为以正交的随机多项式为基底的级数,随机多项式基底的输入变量为描述环境随机性或不确知性的随机变量。利用随机多项式展开基底的正交性可快速估计拷贝场复声压互谱矩阵。仿真结果表明：在声源频率较低、浅海海水声速存在随机起伏的条件下,在计算效率上使用随机多项式展开方法估计拷贝场复声压互谱矩阵较蒙特卡洛统计方法可提高一个数量级;在高信噪比下,多特征向量约束匹配场声源定位算法在定位准确率和输出峰均比上优于线性匹配场和对角加载的最小方差匹配场声源定位方法。     

一种基于双引导声源和warping变换的拷贝声场计算方法

提出了在均匀浅海环境中只知道少量环境参数的情况下,利用不同距离上的两枚引导声源和warping变换的拷贝声场计算方法。这种方法主要基于简正波估计技术,从声场衰减信息和相位信息两个方向来重构拷贝声场,从而与目标声场匹配进行定位。其优点在于能避免传统的匹配场定位技术中拷贝场计算时对环境模型和环境参数的依赖。在简正波估计技术中采用warping变换的处理方式,数值仿真主要采用线性Bartlett匹配处理器分析了目标定位效果,在信噪比高于10~15 dB的情况下,定位效果良好。     

利用拖船辐射噪声的深海声学参数近场匹配场反演方法

针对深海声学参数难以通过远距离合作声源反演获取的问题,提出了利用拖船低频噪声近场匹配场反演方法。首先,利用聚焦波束形成计算拖曳阵接收拖船噪声的方向性,获得传播路径特征;然后,构建多参数反演模型,由波数积分声传播模型计算拷贝场,采用遗传算法对多频匹配场目标函数进行反演。同时,采用蒙特卡罗方法分析参数后验概率密度。仿真与试验结果表明:深海环境中拖曳阵接收拖船噪声主要来自海底反射路径,利用该特性反演得到海水深度、噪声源距离、阵列深度、沉积层厚度等参数,多频联合反演可以提高沉积层厚度等参数反演准确性。宽带匹配场处理表明,利用反演最优参数模型能准确给出拖船噪声源的空间位置。      

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。     

虚拟接收目标定位技术中引导声源对目标声源的信噪比补偿

目标声源的低信噪比是目标定位技术中的瓶颈。在浅海近似均匀环境中,利用虚拟接收方法对声源测距的目标定位方法相比于传统的匹配场定位方法可以避免对环境参数和声场模型的依赖,同时省去生成拷贝场时的大量计算。通过数值模拟和实验数据处理,比较了在虚拟接收方法声源测距中不同信噪比的引导声源对目标声源测距的结果。提高引导声源的信噪比,可以一定程度上提高于涉条纹清晰度,从而提高测距准确度。提高引导声源的信噪比可作为目标声源的信噪比补偿是利用虚拟接收方法的目标定位技术的又一个优点。     

利用宽带引导声源重构拷贝声场的目标声源定位

为了避开传统的匹配场目标定位技术对环境先验知识的依赖性,提出了在均匀浅海环境中只知道少量环境参数的情况下,利用垂直接收阵和不同距离上的两枚宽带引导声源重构声场对目标声源进行定位的一种方法。这种方法主要基于简正波估计和声场重构两种关键技术,同时省去了匹配场定位技术中大量的拷贝声场计算。数值仿真主要采用线性Bartlett匹配处理器分析了目标定位效果,在信噪比高于10 d B的情况下,定位效果良好。     

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。     

深海中利用单水听器的影区声源无源测距测深方法

在典型深海情况下当声源与接收水听器位于海水表层时,在影区内由声源海底接收器、声源海面海底接收器、声源海底海面接收器和声源海面海底海面接收器4条声线形成声场干涉结构,声强随着频率具有两种干涉周期,随着收发距离的增加而增大,分别随着声源深度、接收水听器深度的增加而减小。因此由单水听器记录的声场干涉结构即可实现宽带声源目标的无源测距测深,仿真分析验证了其有效性。在南海深海声学实验中观测到海面宽带噪声源在声场影区所形成的声场干涉结构,数据分析结果验证了深海声场干涉结构用于声源无源定位的有效性。与传统无源定位方法相比,该方法不需要宽带引导声源、精确的海底声学参数和大规模的拷贝场计算。      

基于压缩拷贝场向量的空域滤波器设计

近几十年来,匹配场处理技术得到了广泛深入的研究,并针对实际应用提出了一系列的具体处理算法.当感兴趣的水下目标信号被水面强干扰信号掩蔽时,对水下目标的匹配场处理定位性能显著下降.现有的广义空域滤波器可以抑制水面强干扰,但计算速度较慢并且内存消耗较大.提出了一种基于压缩拷贝场算法的空域滤波器设计方案,并通过非相干叠加处理宽带问题.相对于现有的空域滤波器,当接收阵元数N大于波导中有效简正波号数Q时,该滤波器可以大幅度缩减计算时间、节约运行内存,并且保持了对水面强干扰的抑制性能.针对近岸浅海环境进行了仿真计算,并给出了一些近岸浅海海域试验数据处理结果,验证了该空域滤波器的性能和对计算速度的提升.结果表明,应用基于压缩拷贝向量的矩阵滤波器对强干扰下的弱目标进行宽带非相干匹配场定位,可实现水下目标的有效区分.     

一种水平变化波导中匹配场定位的虚拟时反实现方法

在水平变化波导中,匹配场被动定位的计算量非常大,严重阻碍了其工程应用.本文提出了一种水平变化波导中匹配场定位的虚拟时反实现方法,其抛物方程模型计算网格总数远小于匹配场处理,从而大大减小计算代价.与匹配场处理不同,虚拟时反实现方法是一个利用介质互易性和叠加性的后向传输过程.通过在各水听器位置放置虚拟声源,并在搜索区域产生相应的模糊平面,对各个模糊平面进行相应加权求和,获得的定位模糊平面.利用地中海浅海实验数据验证了虚拟时反实现方法的快速性能.     

Matched-field depth estimation for active sonar

This work concerns the problem of estimating the depth of a submerged scatterer in a shallow-water ocean by using an active sonar and a horizontal receiver array. As in passive matched-field processing (MFP) techniques, numerical modeling of multipath propagation is used to facilitate localization. However, unlike passive MFP methods where estimation of source range is critically dependent on relative modal phase modeling, in active sonar source range is approximately known from travel-time measurements. Thus the proposed matched-field depth estimation (MFDE) method does not require knowledge of the complex relative multipath amplitudes which also depend on the unknown scatterer characteristics. Depth localization is achieved by modeling depth-dependent relative delays and elevation angle spreads between multipaths. A maximum likelihood depth estimate is derived under the assumption that returns from a sequence of pings are uncorrelated and the scatterer is at constant depth. The Cramér-Rao lower bound on depth estimation mean-square-error is computed and compared with Monte Carlo simulation results for a typical range-dependent, shallow-water Mediterranean environment. Depth estimation performance to within 10% of the water column depth is predicted at signal-to-noise ratios of greater than 10 dB. Real data results are reported for depth estimation of an echo repeater to within 10-m accuracy in this same shallow water environment.     

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。      

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.     

An experimental study on matched field processing in shallow water

I.IntroductionPassiverangeanddepthlocalizationofanacousticsourceinshallowwaterisadifficult,yetinterestingproblemthathasreceivedagreatdealofattentioninthelastfewyears['5].Thesimu1taneousestimationofrangeanddepthrequirestheuseofnumericalpropagationmodels.Theclassicalapproachtothisproblemisto"match"thereceivedacousticdatawiththesoundfieldpredictedbythepropagationmodelforanumberofhypotheticalrange/depthsourcelocatiolls.Thistechniqueiscalledmatchedfieldprocessing.Itiscommonlyacceptedthatthewavepro…     

不确定海洋环境中的模态子空间重构稳健定位方法

实际的海洋是一个不确定的声传播环境,常规的匹配场方法在进行目标定位时会遇到环境失配的问题,导致定位性能下降.在不确定的海洋环境中,声场传播中的一部分简正波模态受到声场不确定性的影响较小.基于此,本文提出了一种模态子空间重构的稳健定位方法.该方法使用稳定的模态来重构拷贝场向量,相比于常规匹配场定位方法中使用全阶模态来构造拷贝场向量,其定位结果更加稳健.利用计算机仿真数据和海试数据进行了定位性能分析,并给出了常规匹配场定位方法和稳健最大似然定位方法作为对比.研究结果表明:1)不确定海洋环境中,常规匹配场定位方法即使在较高的信噪比条件下其定位性能也较差.2)模态子空间重构定位方法的性能优于常规匹配场定位方法和稳健最大似然方法.     

Source-Space Compressive Matched Field Processing for Source Localization

Source localization by matched-field processing(MFP) can be accelerated by building a database of Green's functions which however requires a bulk-storage memory.According to the sparsity of the source locations in the search grids of MFP,compressed sensing inspires an approach to reduce the database by introducing a sensing matrix to compress the database.Compressed sensing is further used to estimate the source locations with higher resolution by solving the l_1-norm optimization problem of the compressed Green's function and the data received by a vertical/horizontal line array.The method is validated by simulation and is verified with the experimental data.     

Matched-field replica model optimization and bottom property inversion in shallow water

Matched-field replica models based on an inaccurate knowledge of geoacoustic parameters such as bottom attenuation, shear, and interfacial sound-speed discontinuities, can predict an incorrect number of propagating modes for a shallow-water channel. The resulting degradation in the matched-field ambiguity surface can be substantially reduced by obtaining optimal replica models via modal-sum-limit optimization or bottom-property inversion. The use of these techniques for multi-tone (70, 95, 145, and 195 Hz) source-tow data recorded near San Diego during the first Shallow-Water Evaluation Cell Experiment (SWellEX-1) significantly increased matched-field correlation levels and improved source localization relative to results obtained with a previous nonoptimized model. The predicted number of propagating modes was also reduced substantially. The inversion for bottom properties (attenuation, interfacial sound-speed discontinuities, no shear) provided sediment attenuation estimates which agree well with Hamilton's models and were an order-of-magnitude greater than that used in the nonoptimized model, which accounts for the reduction in the number of modes. A simulated modal decomposition using the inverted optimal replica model verifies the number of modes predicted by the modal-sum-limit optimization.     

Regularized matched-mode processing for source localization

This paper develops a new approach to matched-mode processing (MMP) for ocean acoustic source localization. MMP consists of decomposing far-field acoustic data measured at an array of sensors to obtain the excitations of the propagating modes, then matching these with modeled replica excitations computed for a grid of possible source locations. However, modal decomposition can be ill-posed and unstable if the sensor array does not provide an adequate spatial sampling of the acoustic field (i.e., the problem is underdetermined). For such cases, standard decomposition methods yield minimum-norm solutions that are biased towards zero. Although these methods provide a mathematical solution (i.e., a stable solution that fits the data), they may not represent the most physically meaningful solution. The new approach of regularized matched-mode processing (RMMP) carries out an independent modal decomposition prior to comparison with the replica excitations for each grid point, using the replica itself as the a priori estimate in a regularized inversion. For grid points at or near the source location, this should provide a more physically meaningful decomposition; at other points, the procedure provides a stable inversion. In this paper, RMMP is compared to standard MMP and matched-field processing for a series of realistic synthetic test cases, including a variety of noise levels and sensor array configurations, as well as the effects of environmental mismatch.     

浅海时变声速环境下的自适应匹配场定位算法实现*

针对浅海环境下声速剖面失配引起的匹配场处理器失配问题，提出了一种自适应匹配场定位算法在声速剖面时变环境下的实现方式。将先验声速剖面集简化为经验正交函数表示，结合蒙特卡洛方法与环境扰动约束算法对当下时刻的目标声源进行匹配场定位。本文以某次试验获取的连续20小时的声速剖面数据为研究对象，通过仿真试验对该算法进行验证，结果表明：在先验声速剖面集的半小时之后，利用自适应算法的距离和深度定位成功率较常规匹配场算法有较大提升，其中，深度正确定位概率相对较低。