不确定海洋环境中基于贝叶斯理论的多声源定位算法

环境参数失配导致定位性能大幅度下降是匹配场定位所面临的难题之一。应用贝叶斯理论对环境聚焦，是当前解决该难题的研究热点。环境聚焦方法的实质是将未知环境参数和声源位置联合优化估计，当出现多个目标时，估计的参数会随着声源个数成倍增加，因此不得不利用有限的观测信息来实现众多参数的估计。本文采用最大似然比方法，获得信号源谱和误差项的最大似然估计，实现这些敏感性较弱参数的间接反演，有效降低了反演参数维数和定位算法复杂度。针对遗传算法的早熟和稳定性差的问题，改进了似然函数的经验表达式。将多维后验概率密度在参数起伏变化范围内积分，得到反演参数的一维边缘概率分布，求解最优值的同时进行反演结果的不确定性分析。本文仿真了位于相同距离、不同深度的两个声源，使用仿真实验验证了提出算法的有效性。     

消频散变换反演海底地声参数

提出1种将消频散变换应用到海底地声参数反演的方法。对单一水听器接收声压信号进行消频散处理后,根据群延时差建立代价函数,反演得到主要海底参数,最后根据贝叶斯统计理论给出了待反演地声参数的边缘后验概率密度。对单层波导进行仿真证明这种新方法的有效性。     

由垂直入射脉冲和海底回波反演海底声参数

浅海海底声参数是影响声场传播的重要参量。文中根据信号的相位特性对反演稳定性的影响进行了数值模拟;并于2002年8月在黄海海区进行了海底声参数反演实验。利用垂直入射脉冲和海底回波数据进行海底声参数反演,由于海底回波信号随穿透深度增加而导致回波信号的信噪比降低,为了有效地增加海底声阻抗反演深度,提出平滑分段抽取冲激响应,重建声阻抗剖面的方法。结合Hamilton经验公式,分离海底声速、密度,反演结果与海底采样样本分析值、经验值吻合较好。     

测量海底声学参数的匹配场反演方法

在测量海底声学参数的实际海洋环境中，声源和接收位置的距离这两个参数常常无法准确测量，在这种情况下，需要采用匹配场反演方法来估计海底的声学参数。一般情况下，匹配场反演方法可以归纳为2个组成部分，即海洋声场的声学预报模型和搜索控制策略。文中采用受控制的穷举方法作为搜索控制策略，对1996年中美远黄海试验的实验数据进行了匹配场反演试验，用以测定海底参数，由此得到的海底声学参数与实验中测量的声场衰减进行对比，一致性很好。     

用定深爆炸声源反演海底声学参数

根据2014年在南中国海开展声学试验的定深爆炸宽带声信号数据进行海底地声参数反演.考虑到不同海底声参数对不同声场物理参数的敏感程度不同以及不同海底声参数对不同反演方法的敏感程度亦不同,综合应用2种反演方法得到不同底质声参数:(1)根据接收的直达波和海底反射波计算得到关注海域的海底反射系数进而反演得到海底声阻抗;(2)实验海区的海底地形为大陆坡,选取Hamilton总结的关于沉积物声速与沉积物密度关系的经验公式,结合沉积物声阻抗与沉积物声速、沉积物密度的关系,进而反演得到沉积物声速和沉积物密度.沉积物声学参数的取样测量是在实验室条件下进行的,温度为23℃,大气压1×105Pa,由于沉积物孔隙海水是决定沉积物声速的关键且受温度压强变化的影响显著,本研究利用沉积物声速与孔隙海水声速的比值即使在温度压强变化的情况下较稳定的特点,可对沉积物声速在实验室条件和海底原位条件进行校正.校正到海底温度和压强后,反演结果与沉积物取样的实测结果和Hamilton总结的结果吻合得相当好:(1)声阻抗的反演结果为2.065 6×10~5g/(cm2·s),修正后的沉积物取样结果则为2.046 0×10~5g/(cm~2·s),Hamilton总结的结果为2.238 0×10~5g/(cm~2·s);(2)声速的反演结果为1 482.6m/s,修正后的沉积物取样结果为1 467.5 m/s,Hamilton总结的结果为1 502.8 m/s;(3)密度的反演结果为1.393 2 g/cm3,沉积物取样结果为1.400 0 g/cm~3,Hamilton总结的结果为1.489 0 g/cm3.     

浅海聚焦定位中简化地声模型的应用研究

为了提高定位算法的环境宽容性,聚焦法将环境参数纳入了寻优空间。聚焦法虽然降低了对环境测量的要求,但是反演参数的增加也增加了反演的复杂性。基于海底反射特性,用两个参数对海底进行建模。通过标准的反演测试问题对简化地声模型在浅海聚焦定位中的有效性进行了分析。结果表明:基于简化地声模型的聚焦定位是可行的。在获得正确定位结果的同时,随着地声参数个数的减少,匹配场处理的便捷性得到了提高。文中引入的简化地声模型是聚焦问题中参数最少的地声模型,它可以有效减少聚焦定位参数维数以提升反演的便捷性。同时,简化地声模型在参数敏感性和耦合性上有较好的表现,这些优点可以保证定位结果的稳健性。     

海底沉积物物理参数的声学反演模式

声学反演方程是声学探测沉积物物理参数的基础方程。基于声学理论和统计理论的声速反演沉积物物理参数的两种模式,运用南海海底沉积物声学物理数据验证、比较了两种反演模式,以反演孔隙度、含水量为例,得出基于双参数经验方程反演模式的适用性较强,但精度有待于提高;基于声速理论的反演模式反演南海海底沉积物物理参数有待于进一步完善。     

用射线参数法遥测浅海沉积层声的特性

本文根据射线参数法测量了浅海(水深约40米)沉积层中的声速与层厚。实验中采用爆炸声源,两个水听器,其中一个固定于声源附近,用以计算爆炸开始时间,另一个用于在不同水平距离接收海底及沉积层下界面的反射信号。用磁带记录仪和示波器配以照相记录仪两种方式记录。所得沉积层中声速值与根据底质取样所得声速值基本一致。     

海底沉积物声学的现场测量和取样系统

1 引言 随着海洋开发和军事海洋学的兴起,海底声学参数的现场取得成为海底沉积物物理学方面极为关注的事情.沉积物声速与物理力学参数之间的密切关系,成为利用声学方法反演得到海底工程性质的基础.人们已日益对这方面的认识得到加深.声学技术逐渐成为有效探测海底沉积构造,寻找海底资源的有用工具,特别是声源技术、信号处理、终端显示技术、计算机技术和其他电器零部件的进步,使得采用声学方法直接测量和识别海底沉积物类型、性状、层理成为可能.本项目的提出,至少有如下几个方面的迫切需要.     

建立海洋平台管节点PSN曲线的贝叶斯方法

针对管节点疲劳试验的小样本特点,探讨了建立ＰＳＮ 曲线的贝叶斯方法。在贝叶斯方法中,ＰＳＮ曲线的统计参数作为随机变量处理,首先根据贝叶斯定理求出参数向量的后验概率密度,然后建立ＰＳＮ曲线的贝叶斯方程,最后再编程计算。算例表明,与传统的ＰＳＮ 曲线相比,贝叶斯ＰＳＮ 曲线更加安全可靠     

Robust source localization in shallow water based on vector optimization

Owing to the multipath effect, the source localization in shallow water has been an area of active interest. However, most methods for source localization in shallow water are sensitive to the assumed model of the underwater environment and have poor robustness against the underwater channel uncertainty, which limit their further application in practical engineering. In this paper, a new method of source localization in shallow water, based on vector optimization concept, is described, which is highly robust against environmental factors affecting the localization, such as the channel depth, the bottom reflection coefficients, and so on. Through constructing the uncertainty set of the source vector errors and extracting the multi-path sound rays from the sea surface and bottom, the proposed method can accurately localize one or more sources in shallow water dominated by multipath propagation. It turns out that the natural formulation of our approach involves minimization of two quadratic functions subject to infinitely many nonconvex quadratic constraints. It shows that this problem (originally intractable) can be reformulated in a convex form as the so-called second-order cone program (SOCP) and solved efficiently by using the well-established interior point method, such as the software tool, SeDuMi. Computer simulations show better performance of the proposed method as compared with existing algorithms and establish a theoretical foundation for the practical engineering application.     

Using matched-field processing to estimate shallow-water bottomproperties from shot data taken in the Mediterranean Sea

It is extremely difficult to determine shallow ocean bottom properties (such as sediment layer thicknesses, densities, and sound speeds). However, when acoustic propagation is affected by such environmental parameters, it becomes possible to use acoustic energy as a probe to estimate them. Matched-field processing (MFP) which relies on both field amplitude and phase can be used as a basis for the inversion of experimental data to estimate bottom properties. Recent inversion efforts applied to a data set collected in October 1993 in the Mediterranean Sea north of Elba produce major improvements in MFP power, i.e., in matching the measured field by means of a model using environmental parameters as inputs, even using the high-resolution minimum variance (MV) processor that is notoriously sensitive and usually results in very low values. The inversion method applied to this data set estimates water depth, sediment thickness, density, and a linear sound-speed profile for the first layer, density and a linear sound-speed profile for a second layer, constant sound speed for the underlying half space, array depth, and source range and depth. When the inversion technique allows for the array deformations in range as additional parameters (to be estimated within fractions of a wavelength, e.g., 0.1 m), the MFP MV peak value for the Med data at 100 Hz can increase from 0.48 (using improved estimates of environmental parameters and assuming a vertical line array) to 0.68 (using improved estimates of environmental parameters PLUS improved phone coordinates). The ideal maximum value would be 1.00 (which is achieved for the less sensitive Linear processor). However, many questions remain concerning the reliability of these inversion results and of inversion methods in general     

深海季节性环境变化对半会聚区尺度水面声定位影响分析

针对深海声定位受海洋环境变化影响明显、需考虑测量系统的环境适应性和宽容性设计问题，提出一种评估海水环境变化对定位性能影响的仿真分析方法，将声场计算、误差传播与交会解算联合建模，以西太平洋中纬度海域夏季和冬季环境为代表性场景讨论了季节性环境变化对定位性能的影响方式和影响程度。仿真结果表明，当接收器位于海洋近表层时，在夏季和冬季呈现出两种不同的声信道样式，夏季季节性温跃层影响下的定位精度较差，冬季表面波导影响下的定位精度相对较好，两者均方根误差(RMSE)相差超过50 m；当接收器位于海洋中上层时，直达波有效作用范围的季节性变化引起定位性能差异，冬季定位精度优于夏季，两者RMSE相差15～20 m；当接收器位于海洋近底层时，利用可靠声路径定位精度较高，定位性能季节性变化不明显。研究认为，海水的季节性环境变化能够改变半会聚区尺度水面声定位的声信道特性以及到达声信息、误差传播、交会求解等测量因素，进而对接收深度位于海洋上层的声定位性能产生明显影响。     

Characterization of shallow ocean sediments using the airborne electromagnetic method

Experimental airborne electromagnetic (AEM) survey data collected in Cape Cod Bay are used to derive continuous profiles of water depth, electrical depth, water conductivity, and bottom sediment conductivity. Through a few well-known empirical relationships, the conductivities are used, in turn, to derive density, porosity, sound speed, and acoustic reflectivity of the ocean bottom. A commercially available Dighem III AEM system was used for the survey without any significant modification. The helicopter-borne system operated at 385 and 7200 Hz; both were in a horizontal coplanar configuration. The interpreted profiles show good agreement with available ground truth data. Where no such data are available, the results appear to be very reasonable. Compared with the shipborne electrode array method, the AEM method can determine the necessary parameters at a much higher speed with a better lateral resolution over a wide range of water depths from 0 to perhaps 100 m. The bottom sediment conductivity that can be measured by the AEM method is closely related to physical properties of sediments, such as porosity, density, sound speed, and, indirectly, sediment types that might carry broad implications for various offshore activities.     

Simulations of Matched-Field Processing in a Deep-Water Pacific Environment

Conventional bearing estimation procedures employ planewave steering vectors as replicas of the true field and seek to resolve in angle by maximizing a power function representing the agreement between actual and replica fields. For vertical arrays in oceanic waveguides the received field depends on range and depth, and it is natural to replace the "look-direction" (theta) by a "look-position" (r, z). Thus an environmental model is constructed by specifying ocean depth, sound speed profile, bottom properties, etc., and a propagation model is employed to construct a replica of the field that would be received on the array for a particular source position. The usual estimators (e.g., Bartlett or maximum likelihood) are then used to gauge the agreement between actual and replica fields and the true source position is identified as that position where the agreement is best. The performance of this kind of matched-field processing is strongly affected by the environment. In particular, we demonstrate through simulations that for a deep-water Pacific environment dominated by waterborne paths, ambiguities or sidelobes are associated with convergence zones. In the absence of mismatch between replica and actual fields we find that a 16-element array performs extremely well in low-frequency regimes. Mismatch caused by uncertainties in phone positions, bottom parameters, ocean sound speed, surface and bottom roughness, etc., causes degradation in localization performance. The impact of some of these effects on conventional and maximum likelihood estimators is examined through simulation.     

Robust broadband matched-field processing: performance in shallowwater

An issue of concern for matched-field processing is the strong dependence between performance and precise knowledge about the environmental parameters. A robust matched-field processor based on minimax robust filtering methods was developed. Here, simulation methods are employed to evaluate the performance of the minimax robust method as well as other robust methods for a range-independent shallow water environment. The performance of the robust methods is compared with that of the nominal processor, that is, the processor based on a single set of environmental parameters thought to be closest to the actual. The matched-field processing performance is evaluated in terms of the peak-to-sidelobe ratio. The simulation results indicate that the robust methods provide significant performance improvements over the nominal processor in the presence of uncertainty in water column sound speed, channel depth, and sound speed in the bottom     

深海海面目标单水听器被动测距方法与验证

基于射线理论分析了在深海情况下海面声源产生声场的频率-距离干涉结构,给出了影区内声场频率-距离干涉结构的近似理论表达式,分析得到影区内声场频域干涉周期随收发距离的增加而增大、随着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被动声源距离估计。在南海深海实验中观测到海面宽带噪声源在声场影区形成的声场干涉结构,对实验获得声场干涉结构的处理结果验证了深海声场影区干涉结构用于被动声源距离估计的有效性。与传统的匹配场被动定位方法相比,该方法不需要已知海底声学参数和大规模的拷贝场计算。     

A time series analysis of sound propagation in a strongly multipathshallow water environment with an adiabatic normal mode approach

Measured time series were generated by small omnidirectional explosive sources in a shallow water area. A bottom-mounted hydrophone recorded sound signals that propagated over a sloping bottom. The time series in the 250-500 Hz band were analyzed with a broad-band adiabatic normal mode approach. The measured waveforms contain numerous bottom interacting multipaths that are complicated by the subbottom structure that contains high-velocity layers near the water-sediment interface. Several of the details of the geoacoustic structure and the depth of the water column at the receiver are inferred from comparisons of the measured data to simulated time series. The sensitivity of broad-band matched-field ambiguity surfaces in the range-depth plane for a single receiver to selected waveguide parameters is examined. A consistent analysis is made where the simulated time series are compared to the measured time series along with the single-receiver matched-field localization solutions for ranges out to 5 km. In this range interval, it was found that the peak cross-correlation between the measured and simulated time series varied between 0.84 and 0.69. The difference between the GPS range and the range obtained from the matched-field solution varied from 0 to 63 m. The geoacoustic structure obtained in the analysis consists of an 8-m low-velocity sediment layer over an 8-m high-velocity layer followed by a higher velocity, infinite half-space     

Estimating geoacoustic parameters using matched-field inversion methods

In this paper, we use matched-field inversion methods to estimate the geoacoustic parameters for three synthetic test cases from the Geoacoustic Inversion Techniques Workshop held in May 2001 in Gulfport, MS. The objective of this work is to use a sparse acoustic data set to obtain estimates of the parameters as well as an indication of their uncertainties. The unknown parameters include the geoacoustic properties of the sea bed (i.e., number of layers, layer thickness, density, compressional speed, and attenuation) and the bathymetry for simplified range-dependent acoustic environments. The acoustic data used to solve the problems are restricted to five frequencies for a single vertical line array of receivers located at one range from the source. Matched-field inversion using simplex simulated annealing optimization is initially used to find a maximum-likelihood (ML) estimate. However, the ML estimate provides no information on the uncertainties or covariance associated with the model parameters. To estimate uncertainties, a Bayesian formulation of matched-field inversion is used to generate posterior probability density distributions for the parameters. The mean, covariance, and marginal distributions are determined using a Gibbs importance sampler based on the cascaded Metropolis algorithm. In most cases, excellent results were obtained for relatively sensitive parameters such as wave speed, layer thickness, and water depth. The variance of the estimates increase for relatively insensitive parameters such as density and wave attenuation, especially when noise is added to the data.