浅海声速剖面与移动声源的跟踪定位

在水平非均匀分布的浅海环境中,针对移动声源跟踪时,声速剖面的变化会对声场产生影响,提出了一种利用集合卡尔曼滤波算法的声速剖面跟踪反演和移动声源跟踪定位的方法。首先,将声速剖面进行距离和深度的参数化表示,从而将对声速剖面的跟踪转化为对声速剖面前3阶经验正交函数系数的跟踪;其次,通过将声源状态信息和声速剖面信息表示为状态变量,而将垂直线列阵接收到的声场信息作为测量值建立状态-测量模型,然后利用集合卡尔曼滤波方法对模型状态变量进行跟踪。仿真结果得出:声速剖面跟踪反演的均方根误差和移动声源跟踪定位的绝对误差都非常小,对声源的跟踪定位精度很高。并且通过增加集合样本数、增加接收信号信噪比以及增加接收阵元数目都可以提高跟踪定位结果精度。最后,利用东海实验数据对本方法进行了验证。      

深海上层声速剖面水平非均匀分布对声场会聚区特性预报的影响

针对深海上层声速剖面水平方向弱变化对声场会聚区特征的影响,通过对0°出射声线传播轨迹的分析,提出了只考虑声源及会聚区位置附近声速剖面水平变化实现深海声场会聚区特征准确预报的方法,该方法有助于降低深海声传播调查时声速剖面水平非均匀分布的测量工作量。通过模拟小振幅内波引起深海上层声速剖面水平非均匀变化,对该方法进行了仿真验证。南海声传播试验数据分析表明,当不考虑上层声速剖面的水平非均匀分布时,模型预报的声场会聚区位置及会聚区能量分布与实测结果有一定差异,该差异具有距离累积效应,到第二会聚区时位置累计误差可达3 km,当考虑会聚区宽度内声速剖面水平变化时,模型预报的声场会聚区位置与实测结果非常吻合,第二会聚区位置预报误差小于1 km。     

非等声速信道下的循环神经网络机动目标跟踪模型

针对水下非等声速信道声线弯曲导致传统滤波跟踪轨迹偏差的问题,提出一种循环神经网络的目标跟踪模型。该模型在缺乏声速剖面信息的情况下,通过数据驱动迭代训练,学习输入观测值与输出状态值之间的映射关系,实现目标位置和瞬态特征变化的精确获知。蒙特卡洛仿真实验结果表明,本文模型在非等声速信道下复杂机动场景中相较传统单模型滤波算法以及交互式多模型算法,水平距离跟踪精度分别提升4.06%,1.57%,深度估计精度分别提升0.87%,0.85%。本文模型相较于传统滤波方法具有更高的跟踪精度,并且能够在失配声速分布信道下进行迁移学习,提升模型在失配声场环境下的泛化性。      

声速剖面反演对基阵倾斜失配的敏感性研究

通过推导基阵倾斜失配给声传播时间计算带来的误差,分析了这种参数失配对基于声传播时间的声速剖面反演结果的敏感性。研究结果表明,小角度倾斜时,由基阵倾斜所带来的声速剖面反演误差与倾斜俯仰角近似成正比,与声传播时间近似成反比。当基阵倾斜方位与声源一基阵连线方向一致时带来的误差最大,而垂直时误差最小。理论计算与仿真、实验结果在变化趋势和数量级上具有较好的一致性,为反演误差预报提供了参考。      

声速剖面反演对基阵倾斜失配的敏感性研究

通过推导基阵倾斜失配给声传播时间计算带来的误差,分析了这种参数失配对基于声传播时间的声速剖面反演结果的敏感性。研究结果表明,小角度倾斜时,由基阵倾斜所带来的声速剖面反演误差与倾斜俯仰角近似成正比,与声传播时间近似成反比。当基阵倾斜方位与声源一基阵连线方向一致时带来的误差最大,而垂直时误差最小。理论计算与仿真、实验结果在变化趋势和数量级上具有较好的一致性,为反演误差预报提供了参考。     

南海北部负跃层环境下海底参数声学反演

利用2015年南海北部声学实验的实验数据,对海底声学参数进行反演。在综合分析声速起伏及海底模型对海底反演影响的基础上,通过选择等效的海水声速剖面和海底模型,改进了多参量联合地声反演方法,使得其不仅能解决反演中的多值性问题,还能适用于负跃层起伏环境下的声学反演。即当用匹配场反演海底声速和用传播损失反演海底衰减时,如果温跃层内有内波等随机起伏,可使用传播路径上平均声速剖面和单层等效海底进行声场计算。反演得到的海底声速和密度结果与海底采样测量符合较好,拟合给出了海底衰减系数随频率的非线性经验关系式。反演结果可为南海北部声传播规律研究与应用提供海底参数。      

浅海波导中低频声场干涉简正模态的判别

浅海波导中,低频宽带声场中干涉简正模特性可用于声源定位和环境参数反演,然而实际应用中由于存在声源位置不确知、某些简正模激发较弱、模型参数选取失配等因素的制约,导致干涉简正模阶数的判别存在问题.结合水平线列阵应用,根据阵列接收信号中干涉简正模成分的波束输出角度与距离无关但与干涉简正模阶数和频率相关的波导固有频散特性,提出了一种基于阵元域接收信号自相关函数WARPING变换过滤干涉简正模,进而对其波束输出角度进行模基匹配判别简正模阶数的方法.利用2011年北黄海海域声学实验中坐底布放的32元水平线列阵接收的爆炸声脉冲信号,对方法进行了验证.并由仿真数据分析了声速剖面、海底参数和水深等参数失配及信噪比对方法性能的影响.结果表明水深变化14%以上对干涉简正模波束输出角度的提取值影响最大,可引起方法失效;声速剖面和海底参数在一定失配范围内对方法性能的影响可忽略;方法要求单阵元信噪比大于2 dB.     

浅海声速剖面的匹配波束反演方法

为了快速反演获得浅海声速剖面,利用匹配波束构造了反演代价函数,在此基础上研究了浅海声速剖面的匹配波束反演方法(MBI)。并运用东中国海的实验数据进行实际反演,验证了MBI的可行性和鲁棒性。利用垂直线列阵接收的29个不同位置的爆炸声信号进行了声速剖面反演, MBI反演的结果与实验中直接测量的声速剖面一致,且反演声速剖面的均方根误差小于2 m/s。研究结果表明,MBI比常规匹配场反演(MFI)获得浅海声速剖面更快速、更准确,并且对底质参数的失配具有较高的鲁棒性。     

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。     

南海北部海域声速剖面水平变化环境声学层析

为获取水平变化的海水声速分布,在等效平均海水声速反演基础上,提出了一种声速剖面水平变化环境声学层析方法。首先利用经验正交函数结合匹配场处理方法反演不同距离下的等效平均声速剖面,进而通过逐步迭代重构出随水平距离变化的声速场分布,并通过数值仿真及海上实验两方面验证了声速剖面水平变化环境声学层析方法的可行性及其准确性。结果表明:声速剖面水平变化环境声学层析结果与实验测量声速剖面均方根误差小于0.8 m/s,利用声传播信号基本能较为准确地重构出随距离水平变化的海水声速分布,为进一步层析中尺度三维结构及声呐水声环境保障应用奠定了基础。      

强非线性时间演化声速剖面的序贯反演

受海面波浪起伏、降雨和内波等海洋动力学过程的影响,浅水声速剖面的时间演化具有高度非线性,针对该问题提出使用改进的粒子滤波方法进行声速剖面序贯反演.该方法通过建立声速剖面的经验正交模型(EOF)以及描述声速剖面时间演化特征的状态空间模型,将声速剖面反演问题建模为状态跟踪问题,利用不敏粒子滤波(UPF:Uncented Particle Filter)算法进行声速剖面序贯反演。仿真试验通过实测声速剖面数据和先验地声参数信息产生接收声场数据,再利用模拟声场数据估计声速剖面的时间变化.结果表明,相比于集合卡尔曼滤波(EnKF:Ensemble Kalman Filter),在计算效率等同的情形下,该方法可以在状态参数的时间跳变点保持良好的跟踪性能,一定程度上克服了现有反演算法在跳变点发散的问题,可以有效提高声速剖面反演精度,尤其在声速剖面时变性较强时具有显著优势.      

Tracking-positioning of sound speed profiles and moving acoustic source in shallow water

An ensemble Kalman filter(EnKF) approach is proposed to perform sequential tracking of water column sound speed profile(SSP) using a moving acoustic source. First,the SSPs are discretized in depth and range, and are expressed by the empirical orthogonal functions(EOFs). Second, the acoustic source state information and the first three orders of EOF coefficients are expressed as the state variable, and the acoustic field information received by the vertical line array are the measured values. Successively, the state variables and measured values are used to establish the state-measure model. Last, the EnKF is utilized to track the state variables. The simulation results show that the root mean square error of SSP and the absolute error of source are all small, and thus the acoustic source tracking-positioning has high accuracy. Moreover, increasing the number of sample collection, the signal-to-noise ratio and the number of receiving elements can improve the tracking-positioning results. The method is verified using the experimental data of the East China Sea.     

Sequential Parameter Estimation Using Modal Dispersion Curves in Shallow Water

Existing sequential parameter estimation methods use the acoustic pressure of a line array as observations. The modal dispersion curves are employed to estimate the sound speed profile(SSP) and geoacoustic parameters based on the ensemble Kalman filter. The warping transform is implemented to the signals received by a single hydrophone to obtain the dispersion curves. The experimental data are collected at a range-independent shallow water site in the South China Sea. The results indicate that the SSPs are well estimated and the geoacoustic parameters are also well determined. Comparisons of the observed and estimated modal dispersion curves show good agreement.     

A generalized polynomial chaos based ensemble Kalman filter with high accuracy

As one of the most adopted sequential data assimilation methods in many areas, especially those involving complex nonlinear dynamics, the ensemble Kalman filter (EnKF) has been under extensive investigation regarding its properties and efficiency. Compared to other variants of the Kalman filter (KF), EnKF is straightforward to implement, as it employs random ensembles to represent solution states. This, however, introduces sampling errors that affect the accuracy of EnKF in a negative manner. Though sampling errors can be easily reduced by using a large number of samples, in practice this is undesirable as each ensemble member is a solution of the system of state equations and can be time consuming to compute for large-scale problems. In this paper we present an efficient EnKF implementation via generalized polynomial chaos (gPC) expansion. The key ingredients of the proposed approach involve (1) solving the system of stochastic state equations via the gPC methodology to gain efficiency; and (2) sampling the gPC approximation of the stochastic solution with an arbitrarily large number of samples, at virtually no additional computational cost, to drastically reduce the sampling errors. The resulting algorithm thus achieves a high accuracy at reduced computational cost, compared to the classical implementations of EnKF. Numerical examples are provided to verify the convergence property and accuracy improvement of the new algorithm. We also prove that for linear systems with Gaussian noise, the first-order gPC Kalman filter method is equivalent to the exact Kalman filter.     

Ensemble Kalman filter with the unscented transform

A modification scheme to the ensemble Kalman filter (EnKF) is introduced based on the concept of the unscented transform [S. Julier, J. Uhlmann, H. Durrant-Whyte, A new method for the nonlinear transformation of means and covariances in filters and estimators, IEEE Trans. Automat. Control. 45 (2000) 477-482; S.J. Julier, J.K. Uhlmann, Unscented filtering and nonlinear estimation, Proc. IEEE 92 (2004) 401-422], which therefore will be called the ensemble unscented Kalman filter (EnUKF) in this work. When the error distribution of the analysis is symmetric (not necessarily Gaussian), it can be shown that, compared with the ordinary EnKF, the EnUKF has more accurate estimations of the ensemble mean and covariance of the background by examining the multidimensional Taylor series expansion term by term. This implies that, the EnUKF may have better performance in state estimation than the ordinary EnKF in the sense that the deviations from the true states are smaller. For verification, some numerical experiments are conducted on a 40-dimensional system due to Lorenz and Emanuel [E.N. Lorenz, K.A. Emanuel, Optimal sites for supplementary weather observations: Simulation with a small model, J. Atmos. Sci. 55 (1998) 399-414]. Simulation results support our argument.     

动态水声环境不确定性的预报与估计

水声环境具有强烈的时空易变性,为了解其变化中的规律和产生的不确定性,将POM海洋数值模型和水声传播模型FOR3D进行耦合,建立海洋—声学耦合数值模式。对海区的温盐等环境参数和水下声场进行预报,给出了典型断面的温度垂直结构、实验海区声速剖面和传播损失。同时,采用集合卡尔曼滤波(EnKF)方法结合海洋—声学耦合数值模式分析了水声环境的不确定性,对不同深度与频率下,传播损失的均值与满足90%的可能区间进行了计算,同时给出了不同点声速、传播损失、声纳作用距离的不确定性直方图。和实验数据的对比结果表明该方法具有较高的有效性,能对水声环境的不确定性进行合理的预报与估计。     

History matching of facies distribution with the EnKF and level set parameterization

In this work, we develop a methodology to combine the Ensemble Kalman filter (EnKF) and the level set parameterization for history matching of facies distribution. With given prior knowledge about the facies of the reservoir geology, initial realizations are generated by commonly used software as the prior guesses of the unknown field. Furthermore, level set functions are used to reparameterize these initial realizations. In the reparameterization process, a representing node system is set up, on which the values of level set functions are assigned using Gaussian random numbers. The mean and the standard deviation of the Gaussian random numbers are designed according to the facies proportion, and the sign of the random numbers depends on the facies type at the representing nodes. The values of the level set functions at the other grid nodes are obtained by linear interpolation. The level set functions on the representing nodes are the model parameters of the EnKF state vector and are updated in the data assimilation process. On the basis of our numerical examples for two-dimensional reservoirs with two or three facies, the proposed method is demonstrated to be able to capture the main features of the reference facies distributions.     

地声参数及传播损失不确定性估计与建模

地声参数的不确定性对水声传播具有重要的影响。通过贝叶斯理论建立水声环境不确定性推理模型,理论推导了地声参数的似然函数以及地声参数和传播损失的后验概率密度,并采用MCMC(Markov Chain Monte Carlo)进行了仿真计算,给出了地声参数的二维后验联合概率密度和一维边缘概率密度,在此基础上对传播损失的不确定性进行了估计,得到了传播损失80%的可信区间。仿真和实验结果表明,该方法适用于地声参数反演和不确定性估计,并能获取因地声参数不确定性导致的传播损失不确定性估计。     

基于Huber的高阶容积卡尔曼跟踪算法

为改善高阶容积卡尔曼滤波算法的滤波精度和鲁棒性, 提出了一种新的基于Huber的高阶容积卡尔曼滤波算法. 在采用统计线性回归模型近似非线性量测模型的基础上, 利用Huber M 估计算法实现状态的量测更新. 进一步结合高阶球面-径向容积准则的状态预测模块构成基于 Huber的高阶容积卡尔曼跟踪算法. 重点分析了Huber代价函数的调节因子对算法跟踪性能的影响. 通过对纯方位目标跟踪和再入飞行器跟踪两个实例验证了所提算法的跟踪性能优于传统高阶容积卡尔曼滤波算法.