Uncertainty estimation in simultaneous Bayesian tracking and environmental inversion

This paper develops a Bayesian approach for two related inverse problems: tracking an acoustic source when ocean environmental parameters are unknown, and determining environmental parameters using acoustic data from an unknown (moving) source. The formulation considers source and environmental parameters as unknown random variables constrained by noisy acoustic data and by prior information on parameter values (e.g., physical limits for environmental properties) and on inter-parameter relationships (limits on radial and vertical source speed). The goal is not simply to estimate parameter values, but to rigorously determine parameter uncertainty distributions, thereby quantifying the information content of the data/prior to resolve source and environmental parameters. Results are presented as marginal posterior probability densities (PPDs) for environmental parameters and joint marginal PPDs for source ranges and depths. Given the numerically intensive inversion, an efficient Markov-chain Monte Carlo importance-sampling approach is developed which combines Metropolis and heat-bath Gibbs' sampling, employs efficient proposal distributions based on a linearized PPD approximation, and considers nonunity sampling temperatures to ensure a complete parameter search. The approach is illustrated with two simulated examples representing tracking a quiet submerged source and geoacoustic inversion using noise from an unknown ship of opportunity. In both cases, source, seabed, and water-column parameters are unknown.     

Inversion for acoustic impedance of a wall by using artificial neural network

A new approach for measuring acoustic impedance is developed by using artificial neural network (ANN) algorithm. Instead of using impedance tube, a rectangular room or a box is simulated with known boundary conditions at some boundaries and an unknown acoustic impedance at one side of the wall. A training data basis for the ANN algorithm is evaluated by similar source method which was developed earlier by Too and Su [Too G-PJ, Su T-K. Estimation of scattering sound field via nearfield measurement by source methods. Appl Acoust. 1999;58:261-81 (SCI) (EI)] for the estimation of interior and exterior sound field. The training data basis is constructed by evaluating of acoustic pressure at a field point with various acoustic impedance conditions at one side of the wall. Then, the inversion for unknown acoustic impedance of a wall is performed by measuring several field data and substituting these data into ANN algorithm. The simulation result indicates that the prediction of acoustic impedance is very accurate with error percentage under 1%. In addition, one field point measurement in the present approach for acoustic impedance provides more straightforward and easier evaluation than that in the two point measurement of impedance tube.     

A statistical geoacoustic inversion scheme based on a modified radial basis functions neural network

This paper addresses the task of recovering the geoacoustic parameters of a shallow-water environment using measurements of the acoustic field due to a known source and a neural network based inversion process. First, a novel efficient "observable" of the acoustic signal is proposed, which represents the signal in accordance with the recoverable parameters. Motivated by recent studies in non-Gaussian statistical theory, the observable is defined as a set of estimated model parameters of the alpha-stable distributions, which fit the marginal statistics of the wavelet subband coefficients, obtained after the transformation of the original signal via a one-dimensional wavelet decomposition. Following the modeling process to extract the observables as features, a radial basis functions neural network is employed to approximate the vector function that takes as input the observables and gives as output the corresponding set of environmental parameters. The performance of the proposed approach in recovering the sound speed and density in the substrate of a typical shallow-water environment is evaluated using a database of synthetic acoustic signals, generated by means of a normal-mode acoustic propagation algorithm.     

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。     

Acoustic emission localization in complex dissipative anisotropic structures using a one-channel reciprocal time reversal method

This paper presents an imaging method for the localization of the impact point in complex anisotropic structures with diffuse field conditions, using only one passive transducer. The proposed technique is based on the reciprocal time reversal approach (inverse filtering) applied to a number of waveforms stored into a database containing the experimental Green's function of the structure. Unlike most acoustic emission monitoring systems, the present method exploits the benefits of multiple scattering, mode conversion, and boundaries reflections to achieve the focusing of the source with high resolution. Compared to a standard time reversal approach, the optimal refocusing of the back propagated wave field at the impact point is accomplished through a "virtual" imaging process. The robustness of the inverse filtering technique is experimentally demonstrated on a dissipative stiffened composite panel and the source position can be retrieved with a high level of accuracy in any position of the structure. Its very simple configuration and minimal processing requirements make this method a valid alternative to the conventional imaging Structural Health Monitoring systems for the acoustic emission source localization.     

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.     

浅海波导环境失配下的协方差矩阵拟合稳健匹配场声源功率估计

实际浅海波导中环境噪声为相干噪声,最小方差匹配场声源功率估计方法能在相干噪声背景下准确估计声源辐射功率,但该方法受环境不确定性影响较大;此外,由于最小方差匹配场声源功率估计方法使用信号幅度作为中间量估计声源功率,信号幅度估计误差会二次放大并传递到声源功率估计结果中。本文提出一种协方差矩阵拟合稳健最小方差匹配场声源功率估计方法,该方法引入信道传递函数不确定集,结合协方差矩阵拟合思想将声源功率估计问题建模为在信道传递函数不确定集约束下对函数取极值的问题,使用Lagrange乘子法求解该问题得到信道传递函数估计值和声源辐射功率估计值。环境失配影响声源辐射功率估计性能的根本原因在于信道传递函数偏差较大,协方差矩阵拟合稳健匹配场声源功率估计方法有效减小了环境失配时信道传递函数的偏差,从而显著提升环境失配稳健性。此外,该方法使用权值直接估计声源功率,无需使用信号幅度作为中间量,避免了估计误差的传递。仿真验证了协方差矩阵拟合稳健匹配场声源功率估计方法的环境失配稳健性。     

Bayesian Tracking in an Uncertain Shallow Water Environment

A Bayesian source tracking approach is developed to track a moving acoustic source in an uncertain ocean environment.This approach treats the environmental parameters(e.g.,water depth,sediment and bottom parameters) at the source location and the source parameters(e.g.,source depth,range and speed) as unknown random variables that evolve as the source moves.To track a target with low signal-to-noise ratio(SNR),acoustic signals from a series of observations are treated in a simultaneous inversion.This allows real-time updating of the environment and accurate tracking of the moving source.The noise signals radiated from a surface ship target are processed and analyzed.It is found that the Bayesian source tracking method could enhance the localization accuracy in an uncertain water environment and low SNR.     

基于时域多普勒修正的运动声全息识别方法

由于运动声源测量信号中多普勒效应的存在,一般声全息方法无法直接使用,而阵列信号波束成形处理方法无法进行定量分析.本方法建立了基于测量面、辐射面和全息面的运动学几何关系,提出了声源与测量信号之间的非线性时间映射方法,基于运动声源的声源特征函数,构造了消除多普勒效应的全息面时域声压分布.全息重建得到运动声源表面有效声压分布,实现了对主要声源处声压幅值的定量估计.实际运动声源的测量实验结果证明了该方法的有效性.     

联合波叠加法的全息理论与实验研究

当空间声场中同时存在多个相干声源时，运用常规近场声全息方法无法重建每个相干声源表面的声学信息，当然也无法预测每个声源单独产生的空间声场，相干声场的全息重建与预测已成为全息技术推广应用过程中亟待解决的问题.在提出联合波叠加法并将其应用于空间声场变换的基础上，对其进行了实验研究.通过对实际相干声场的全息重建与预测，验证了常规波叠加法在相干声场重建中的局限性、联合波叠加法在相干声场全息重建与预测过程的可行性和准确性，还研究了Tikhonov正则化方法在抑制声学逆问题的非适定性中的有效性和滤波系数的选择原则的可行性，以提高全息重建与预测的精度. 关键词： 近场声全息 联合波叠加 相干声场 Tikhonov正则化     

Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.     

Source ranging with minimal environmental information using a virtual receiver and waveguide invariant theory

A method is presented for estimating the range of an unknown broadband acoustic source in a waveguide, using a vertical array and a signal sample from another broadband source at a known location relative to the array. The method requires no modeling of the acoustic field, and little to no environmental information for flat bathymetries. Waveguide invariant theory [e.g., D'Spain and Kuperman, J. Acoust. Soc. Am 106, 2454-2468 (1999)] is applied to the "virtual receiver" [Siderius et al., J. Acoust. Soc. Am. 102, 3439-3449 (1997)] to create a "virtual aperture" (VA). In effect, the method effectively converts a source at known range r(g) into a continuum of receivers lying between ranges (1 +/- alpha/beta)*r(g), where beta is a scalar parameter called the acoustic invariant, and alpha approximately 0.1. This effective displacement is achieved by correlating the known source field, measured at frequency component omega, with the unknown source field, measured at frequency component omega + omegas. When the VA output is plotted as a function of omega and omegas, the slope of the resulting correlation contours yields the unknown source range. The concept is illustrated via both simulation and analysis of data collected from a pseudo-random noise source with 75-150-Hz bandwidth during SWellEx-3, a shallow water experiment conducted off the San Diego coast. The virtual aperture can be reformulated for range-dependent environments, if adiabatic propagation assumptions are valid, and if the bathymetry surrounding the array is known.     

利用源强密度声辐射模态重建声场

为了利用声场中少量测点声压数据精确重建复杂结构的辐射声场,提出了源强密度声辐射模态分析理论和声场重建公式.在结构表面定义的空间上,利用以源强密度分布函数为参量的结构辐射声功率泛函表达式定义了一个线性自伴正辐射算子,该算子的特征函数为结构的源强密度声辐射模态.然后通过对矩形平板和带有半球帽的圆柱体的源强密度声辐射模态的分析,证明了源强密度声辐射模态具有空间滤波特性,并利用该性质建立了声场重建公式.球体仿真和平板实验验证了所提出的声场重建方法的可行性和稳健性.基于源强密度声辐射模态的声场重建方法简单,利用较少测点数据就可以获得较高的声场重建精度,特别适合于复杂结构的低频声场重建.     

A robust super-resolution approach with sparsity constraint in acoustic imaging

Acoustic imaging is a standard technique for mapping acoustic source powers and positions from limited observations on microphone sensors, which often causes an ill-conditioned inverse problem. In this article, we firstly improve the forward model of acoustic power propagation by considering background noises at the sensor array, and the propagation uncertainty caused by wind tunnel effects. We then propose a robust super-resolution approach via sparsity constraint for acoustic imaging in strong background noises. The sparsity parameter is adaptively derived from the sparse distribution of source powers. The proposed approach can jointly reconstruct source powers and positions, as well as the background noise power. Our approach is compared with the conventional beamforming, deconvolution and sparse regularization methods by simulated, wind tunnel data and hybrid data respectively. It is feasible to apply the proposed approach for effectively mapping monopole sources in wind tunnel tests.     

Reconstruction and prediction of coherent acoustic field with the combined wave superposition approach

The routine wave superposition approach cannot be used in reconstruction and prediction of a coherent acoustic field, because it is impossible to separate the pressures generated by individual sources. According to the superposition theory of the coherent acoustic field , a novel method based on the combined wave superposition approach is developed to reconstruct and predict the coherent acoustic field by building the combined pressure matching matrixes between the hologram surfaces and the sources. The method can reconstruct the acoustic information on surfaces of the individual sources, and it is possible to predict the acoustic field radiated from every source and the total coherent acoustic field can also be calculated spontaneously. The experimental and numerical simulation results show that this method can effectively solve the holographic reconstruction and prediction of the coherent acoustic field and it can also be used as a coherent acoustic field separation technique. The study on this novel method extends the application scope of the acoustic holography technique.     

Separation of acoustic modes in the Florida Straits using noise interferometry

We consider separation of acoustic modes in an experiment carried out in the Florida Straits. The features of the approach are separation of modes using data from single hydrophones, not vertical mode arrays, and a passive scheme of noise interferometry in which the source consists of ocean noise. Processing made it possible to reliably separate the first four modes of the acoustic field. The results allow a conclusion on the possible use of this method for shallow-water monitoring under complex hydrological conditions.     

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。      

Bayesian localization in an uncertain ocean environment

In order to improve the ability to localize a source in an uncertain acoustic environment,a Bayesian approach,referred to here as Bayesian localization is used by including the environment in the parameter search space.Genetic algorithms are used for the parameter optimization.This method integrates the a posterior probability density(PPD) over environmental parameters to obtain a sequence of marginal probability distributions over source range and depth,from which the most-probable source location and localization uncertainties can be extracted.Considering that the seabed density and attenuation are less sensitive to the objective function of matched field processing,we utilize the empirical relationship to invert those parameters indirectly.The broadband signals recorded by a vertical line array in a Yellow Sea experiment in 2000 are processed and analyzed.It was found that,the Bayesian localization method that incorporates the environmental variability into the processor,made it robust to the uncertainty in the ocean environment.In addition,using the empirical relationship could enhance the localization accuracy.     

浅海宽带简正模相干/非相干能量比值特征匹配的源深估计

无源声源深度估计一直是水声领域的一个难题。针对浅海波导中低频宽带脉冲声源深度估计问题,提出了利用单水听器接收信号中多阶简正模相干项能量与非相干项能量比值的特征匹配处理方法,不仅消除了声源未知激发谱的影响,而且由于在预先估计声源距离基础上实现对声源深度的独立估计,运算量小。同时,由于利用了实测数据与拷贝声场中具有相同简正模贡献的相干能量特征进行匹配,参数估计具有较高的稳健性。首先通过向量线性相关数学理论,在理想波导条件下讨论了简正模数目与源深估计单值性的关系,证明了仅利用两阶简正模时源深估计具有多值性,而声场含有三阶以上简正模时源深估计存在唯一解。进一步的仿真分析表明,当信噪比高于0 dB时源深估计结果的平均误差整体上在5 m之内。分析了水文参数、沉积层声学参数及声源距离失配时方法的鲁棒性。最后,利用浅海垂直阵采集的低频宽带脉冲实验数据对方法进行验证,声源深度估计结果绝对误差小于5 m的概率在79.1%以上。方法可推广应用于浅海水平或垂直基阵对低频宽带声源的深度估计。      

An inversion modelling method to obtain the acoustic power of the noise sources in a large factory

A common problem for large factories that wish to decrease their environmental acoustic impact on neighbouring locations is to find out the acoustic power of every noise source. As these factories cannot stop their activity in order to measure each source individually, a procedure is needed to obtain the acoustic powers with the factory under normal operating conditions. Their contribution to the overall sound pressure level at each neighbouring location can then be found and it is possible to calculate the improvements obtained after any modification of the sources. In this paper an inversion modelling method is used to do so. Acoustic powers are obtained by means of field sound pressure level measurements and with the use of a sound propagation software. A careful analysis of the solution has been carried out by simulating errors on the measured data in order to detect possible correlations between the acoustic power of different sources and avoid misleading interpretations of the results. The whole methodology has been applied to a liquid-gas production factory.