捕捉环境不确实性的声学—动力数据同化技术
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摘要
海洋是一个复杂的流体动力学系统,其内部存在着形式多样的动力过程,如锋面、涡流、内波等。这些过程以及它们之间的耦合,加上与海床的相互作用,导致海洋环境时间与空间上的显著变化,存在所谓的环境不确实性。这些海洋环境的不确实性和水声传播及声纳信号处理之间存在着紧密的联系。首先环境不确实性使得声压场在空间和时间上具有不确实性和变化性,而声压场的不确实性和变化性又使得声纳信号处理存在较大的不确实性。比如,目前水下被动源定位的主要手段-匹配场处理(Matched-Field Processing-MFP)利用了声在海洋波导中的传播特性与观察数据,但对于环境的不确实性和变化性非常敏感。如果可用的环境信息不够准确,即使在信噪比非常高的情况下MFP性能也会明显下降。因此,当今水声研究的一个主要方向是量化各种物理过程对声传播的影响,捕捉环境不确实性,进而减小环境不确实性对声纳系统工作性能的影响。
通常环境参数可由仪器设备直接测量得到,但是由于现场观测资源和能力有限,获取长期、大范围空间、时间充分采样的数据是不现实的。数据同化通过融合观察数据和海洋动力系统,将不同性质的测量数据有效地结合起来,给出相关尺度上与观测数据吻合的动力过程的确定性描述,是目前综合海洋环境监测常用的手段之一。由于声传播包含了丰富的海洋温度场、流场分布信息,声学数据为传统同化技术提供了新的有效数据来源,同时声学-动力数据同化也为捕捉环境不确实性问题提出了一种新的手段。
论文针对环境不确实性对声纳系统工作性能的影响,在综合、分析和评估哈佛海洋预测系统(Harvard Ocean Prediction System-HOPS)和自适应快速环境评估系统的基础上,开展基于声学-动力数据同化技术的海洋环境与声场预测理论及方法研究,旨在为解决在实际海洋环境背景下,通过动态建模,动态测量、量化和融合,以及模型与数据的结合,获取既灵敏又宽容的海洋观测的信息科学问题提供思路。一方面,声学-动力数据同化技术融合了高分辨力的环境测量数据以及声学测量数据,将动力系统工作尺度建模到声传播建模所要求的较小尺度上。另一方面,根据系统特定的应用模式建立了声学测量模型,并耦合数据同化结果进行误差分析以及声学测量性能分析,将分析结果用于环境观测网络资源部署的优化。
全文根据声学-动力数据同化技术的基本框架,分别对海洋动态建模、数据同化算法、声学测量模型建模与移动观测系统优化部署算法这几个问题展开了深入的研究,主要包括以下三方面的结果。
1.根据特定海区与内波建模有关的实测数据,通过对传统Garrett-Munk模型的修正发展了适用于此海区的工作尺度在100m量级的浅海线性内波模型,并给出了内波影响下与声学测量密切相关的声速动态演化特性。同时,论文详细描述了用于大尺度海洋环境预测的HOPS实现框架、功能原理及使用方法,并以Massachusetts Bay区域为例,给出了HOPS的预测结果。HOPS预测结果可作为声学-动力数据同化系统的初始化及边界条件。
2.针对海洋中声速场复杂的空-时演化特性,发展了基于声速局部测量、声速动力模型、声场传播模型以及声压测量这四个方程的声学-动力数据通用框架,并且在此基础上采用多点扰动法、集合卡尔曼滤波(Ensemble Kalman Filter-EnKF)以及无迹卡尔曼滤波(Uscented Kalman Filter-UKF)等方法实现了声速场不确实性估计。基于多点扰动法的反演算法将内波动力模型代入通用框架,根据最小均方误差准则求解代价函数,从而获得声速场的估计结果。该方法能够很好地应用于存在内波扰动的非线性系统,然而需要对每一维估计参数在可能的分布区域进行搜索,在高维情况下计算复杂度较高。考虑到算法的计算效率,论文采用经验正交函数(Empirical Orthogonal Functions-EOF)来描述声速场不确实性,并且基于序贯滤波思想将声学-动力数据同化技术的通用框架建模成状态-空间模型。由于直接通过海洋动力模型推导显式的EOF系数随时间演化的状态方程较为困难,这里根据海洋动力模型或实测声速数据通过自回归分析拟合方法得到EOF系数的高阶时间演化模型。相比于传统的一阶假设建模,仿真结果表明基于高阶状态方程的EnKF算法和UKF算法具有更优的估计性能。
3.将系统具体的应用模式设定为减少声速场预测不确实性、减少声场预测不确实性以及目标源定位问题这三种情况,并且开展了对应模式下的移动观测系统优化部署算法研究。移动观测系统优化部署算法的主要思路是根据不同的应用模式建立对应的目标函数,并且将最小化目标函数转化为求取最短路径问题从而获取水下自主航行器的运行路径。针对第一种应用模式,直接将数据同化算法的估计结果用于卡尔曼滤波器产生声速场预测的后验不确实性,并将此定义为目标函数。针对第二种应用模式,需要解决声速场不确实性向声场不确实性的传递问题。考虑到在线处理的需要,采用线性近似法计算随机声场的统计信息,将待估区域声传播损失的方差信息作为目标函数。针对第三种应用模式,根据Bayesian框架下的克莱梅-罗下限的参量估计耦合性建立声学测量性能的误差分析模型,并将与声源估计耦合性较大的环境参数的不确实性作为目标函数。通过PRIMER实验环境中的仿真分析了不同应用模式下的移动观测系统优化部署算法的效果。
论文声学-动力数据同化技术的部分理论内容采用2006年SW06实验采集数据和2011年千岛湖实验的实测数据进行了验证。处理结果表明将声学-动力数据同化技术用于捕捉海洋环境中声速场不确实是可行并有效的。基于减少声速场预测不确实性应用模式的移动观测系统优化部署算法也采用HOPS输出的Massachusetts Bay海域环境数据进行了验证,说明该路径规划方法是有效的。
The ocean is a complicated hydro-dynamic system, showing various forms of dy-namic processes exist, such as internal waves, eddies, and fronts. These processes and their inter-coupling and interactions with the seabed make the ocean environment highly variable in time and space, which contributes to the so-called environmental uncertainty. There is a close connection between the ocean environmental uncertainty and sonar signal processing. First, environmental uncertainty cause the sound pressure field showing uncer-tainty and variability in space and time, which can then severely affect the performance of sonar signal processing. For example, the main technique for passive source localiza-tion, matched-field processing (MFP), is concerned with exploiting the combination of the acoustic propagation physics in an ocean waveguide and the observation data; its perfor-mance is rather sensitive to the environmental uncertainty. If the available environmental information is not sufficiently accurate, MFP would break down even if the signal-to-noise ratio is high. Thus, a major research direction for underwater acoustics today is to quan-tify the effects of a variety of physical processes on the acoustic propagation, capture the environmental uncertainty, and reduce the impact of the environmental uncertainty on the performance of a sonar system.
Environmental parameters can be directly measured by the instruments. However, carrying out a large area and long-term observation with sufficient temporal and spatial sampling is unrealistic given limited in situ measurement resources and capabilities. Da-ta assimilation by assimilating instant observed data of different natures to oceanograph-ic dynamical system, provides a deterministic description of the dynamic process, which is consistent with the observation data on the relevant scales. As such, it is becoming a common technique for integrated marine environmental monitoring. Because the acoustic propagation contains a wealth of distribution information of the ocean temperature and flow field, the acoustic data provides a new effective data source for data assimilation; in the meantime, acoustic-dynamical data assimilation renders a new technical framework for capturing environmental uncertainty.
Targeting at the impact of the environmental uncertainty on the performance of sonar systems, this thesis focuses on both ocean environmental and acoustic field prediction the-ory and methods research in the framework of the acoustic-dynamical data assimilation. The approach here learned from the Harvard Ocean Prediction System (HOPS) and the Adaptive Rapid Environmental Assessment, and aims to render a train of thoughts for obtaining both sensitive and robust ocean observing information through dynamical mod-eling, dynamical measurements, quantification and fusion, as well as the combination of model and data in the context of real ocean applications. On one hand, acoustic-dynamical data assimilation melds high-resolution environmental measurements and acoustic mea-surements, and models the dynamical system on a relatively small scale required by the acoustic propagation modeling. On the other hand, the acoustic measurement model can be built according to specific system applications, and then coupled to data assimilation results to analyze the error and acoustic measurement performance, which can be used for optimizing the resource deployment of an environmental observational network.
Following the basic framework of the acoustic-dynamical data assimilation, this the-sis has conducted research on the following subjects:oceanographic dynamical modeling, data assimilation algorithm, acoustic measurement modeling, and optimum deployment algorithm for mobile observational systems. The main results can be summarized in three aspects.
1. A modified Garrett-Munk model for the internal wave with the scale on the or-der of100m is introduced as an oceanographic dynamical model based on measurement data from specific shallow water sites relevant to internal wave modeling. The evolution characteristics of sound speed under internal wave perturbations, which is closely related to acoustic measuring process, are then discussed. In the meantime, the implementation framework, function principles, and application methods of the HOPS for large-scale o-cean environmental prediction are described in detail. The prediction results of the HOPS, using the Massachusetts Bay area as an example site, are presented. HOPS prediction re-sults can be used as the initialization and boundary conditions for the acoustic-dynamical data assimilation system.
2. A general framework of the acoustic-dynamical data assimilation is presented, which includes a local sound speed measurement model, an oceanographic dynamical model, a local acoustic pressure measurement model, and an acoustic propagation model. Based on the general framework, three algorithms are developed to estimate the uncertain-ty of sound speed field:the traditional variational approach, the ensemble Kalman filter (EnKF), and the unscented Kalman filter (UKF). For the traditional variational approach, a linear internal wave model is included, and the sound speed is estimated by minimizing the mean square errors of those four models, called the cost function. This method can be applied to nonlinear systems with internal wave disturbances. However, it requires to search for each dimension of the unknown parameters in the possible distribution space, which would result in very high computational complexity for a high dimensional param-eter set. In order to reduce the computational load in data assimilation, the sound speed perturbations are described by the empirical orthogonal functions (EOF), and the general framework of the acoustic-dynamical data assimilation is modeled as a state-space mod-el following the concept of sequential filtering. However, it is difficult to directly derive an explicit, time-varying EOF state equation from an oceanographic dynamical model. Instead, auto-regressive analysis method is introduced here to obtain high-order state evo-lution model of the EOF coefficients based on the oceanographic dynamical model or the sound speed measurements. Compared with the traditional first-order state evolution mod-el, the estimated results of EnKF and UKF based on the high-order state evolution model show better performance.
3. The optimum deployment algorithm for mobile observational systems is conduct-ed for three specific application modes:reducing sound speed field prediction uncertain-ty, reducing acoustic field prediction uncertainty, and improving target localization. The main idea is to create the corresponding objective function, and then minimize the objec- tive function to get the Autonomous Underwater Vehicle path through some very efficient shortest path algorithms. For the first application mode, the objective function is the poste-rior sound speed field uncertainty, which is calculated through the Kalman filter based on the estimated results of the data assimilation implementation. For the second application mode, the objective function is the posterior acoustic field prediction uncertainty. Here, we need to describe the propagation of both environmental and sound speed uncertainty to acoustic field uncertainty. Considering the need for online processing, a linear approxima-tion method is applied to calculate the variance of sound transmission loss, which is defined as the acoustic field prediction uncertainty. For the third application mode, the Cramer-Rao Bound in the Bayesian framework is used to establish acoustic measurement error and cou-pling model, and the uncertainty of environmental parameters strongly coupled with source range is defined as the objective function. The effectiveness of the optimum deployment al-gorithm for different application modes are analyzed via simulations in a realistic shallow water environment selected from the Shelf Break PRIMER experiment.
Finally, experimental data from the SW06Experiment in2006and the Qiandao-Lake experiment in2011are processed to validate some of the theoretical developments. Ex-perimental data processing results have verified the feasibility and effectiveness of the acoustic-dynamical data assimilation for capturing environmental uncertainty. The opti-mum deployment algorithm for mobile observational systems based on reducing sound speed field prediction uncertainty is also validated using the output data from the HOPS running for the region of the Massachusetts Bay.
通常环境参数可由仪器设备直接测量得到,但是由于现场观测资源和能力有限,获取长期、大范围空间、时间充分采样的数据是不现实的。数据同化通过融合观察数据和海洋动力系统,将不同性质的测量数据有效地结合起来,给出相关尺度上与观测数据吻合的动力过程的确定性描述,是目前综合海洋环境监测常用的手段之一。由于声传播包含了丰富的海洋温度场、流场分布信息,声学数据为传统同化技术提供了新的有效数据来源,同时声学-动力数据同化也为捕捉环境不确实性问题提出了一种新的手段。
论文针对环境不确实性对声纳系统工作性能的影响,在综合、分析和评估哈佛海洋预测系统(Harvard Ocean Prediction System-HOPS)和自适应快速环境评估系统的基础上,开展基于声学-动力数据同化技术的海洋环境与声场预测理论及方法研究,旨在为解决在实际海洋环境背景下,通过动态建模,动态测量、量化和融合,以及模型与数据的结合,获取既灵敏又宽容的海洋观测的信息科学问题提供思路。一方面,声学-动力数据同化技术融合了高分辨力的环境测量数据以及声学测量数据,将动力系统工作尺度建模到声传播建模所要求的较小尺度上。另一方面,根据系统特定的应用模式建立了声学测量模型,并耦合数据同化结果进行误差分析以及声学测量性能分析,将分析结果用于环境观测网络资源部署的优化。
全文根据声学-动力数据同化技术的基本框架,分别对海洋动态建模、数据同化算法、声学测量模型建模与移动观测系统优化部署算法这几个问题展开了深入的研究,主要包括以下三方面的结果。
1.根据特定海区与内波建模有关的实测数据,通过对传统Garrett-Munk模型的修正发展了适用于此海区的工作尺度在100m量级的浅海线性内波模型,并给出了内波影响下与声学测量密切相关的声速动态演化特性。同时,论文详细描述了用于大尺度海洋环境预测的HOPS实现框架、功能原理及使用方法,并以Massachusetts Bay区域为例,给出了HOPS的预测结果。HOPS预测结果可作为声学-动力数据同化系统的初始化及边界条件。
2.针对海洋中声速场复杂的空-时演化特性,发展了基于声速局部测量、声速动力模型、声场传播模型以及声压测量这四个方程的声学-动力数据通用框架,并且在此基础上采用多点扰动法、集合卡尔曼滤波(Ensemble Kalman Filter-EnKF)以及无迹卡尔曼滤波(Uscented Kalman Filter-UKF)等方法实现了声速场不确实性估计。基于多点扰动法的反演算法将内波动力模型代入通用框架,根据最小均方误差准则求解代价函数,从而获得声速场的估计结果。该方法能够很好地应用于存在内波扰动的非线性系统,然而需要对每一维估计参数在可能的分布区域进行搜索,在高维情况下计算复杂度较高。考虑到算法的计算效率,论文采用经验正交函数(Empirical Orthogonal Functions-EOF)来描述声速场不确实性,并且基于序贯滤波思想将声学-动力数据同化技术的通用框架建模成状态-空间模型。由于直接通过海洋动力模型推导显式的EOF系数随时间演化的状态方程较为困难,这里根据海洋动力模型或实测声速数据通过自回归分析拟合方法得到EOF系数的高阶时间演化模型。相比于传统的一阶假设建模,仿真结果表明基于高阶状态方程的EnKF算法和UKF算法具有更优的估计性能。
3.将系统具体的应用模式设定为减少声速场预测不确实性、减少声场预测不确实性以及目标源定位问题这三种情况,并且开展了对应模式下的移动观测系统优化部署算法研究。移动观测系统优化部署算法的主要思路是根据不同的应用模式建立对应的目标函数,并且将最小化目标函数转化为求取最短路径问题从而获取水下自主航行器的运行路径。针对第一种应用模式,直接将数据同化算法的估计结果用于卡尔曼滤波器产生声速场预测的后验不确实性,并将此定义为目标函数。针对第二种应用模式,需要解决声速场不确实性向声场不确实性的传递问题。考虑到在线处理的需要,采用线性近似法计算随机声场的统计信息,将待估区域声传播损失的方差信息作为目标函数。针对第三种应用模式,根据Bayesian框架下的克莱梅-罗下限的参量估计耦合性建立声学测量性能的误差分析模型,并将与声源估计耦合性较大的环境参数的不确实性作为目标函数。通过PRIMER实验环境中的仿真分析了不同应用模式下的移动观测系统优化部署算法的效果。
论文声学-动力数据同化技术的部分理论内容采用2006年SW06实验采集数据和2011年千岛湖实验的实测数据进行了验证。处理结果表明将声学-动力数据同化技术用于捕捉海洋环境中声速场不确实是可行并有效的。基于减少声速场预测不确实性应用模式的移动观测系统优化部署算法也采用HOPS输出的Massachusetts Bay海域环境数据进行了验证,说明该路径规划方法是有效的。
The ocean is a complicated hydro-dynamic system, showing various forms of dy-namic processes exist, such as internal waves, eddies, and fronts. These processes and their inter-coupling and interactions with the seabed make the ocean environment highly variable in time and space, which contributes to the so-called environmental uncertainty. There is a close connection between the ocean environmental uncertainty and sonar signal processing. First, environmental uncertainty cause the sound pressure field showing uncer-tainty and variability in space and time, which can then severely affect the performance of sonar signal processing. For example, the main technique for passive source localiza-tion, matched-field processing (MFP), is concerned with exploiting the combination of the acoustic propagation physics in an ocean waveguide and the observation data; its perfor-mance is rather sensitive to the environmental uncertainty. If the available environmental information is not sufficiently accurate, MFP would break down even if the signal-to-noise ratio is high. Thus, a major research direction for underwater acoustics today is to quan-tify the effects of a variety of physical processes on the acoustic propagation, capture the environmental uncertainty, and reduce the impact of the environmental uncertainty on the performance of a sonar system.
Environmental parameters can be directly measured by the instruments. However, carrying out a large area and long-term observation with sufficient temporal and spatial sampling is unrealistic given limited in situ measurement resources and capabilities. Da-ta assimilation by assimilating instant observed data of different natures to oceanograph-ic dynamical system, provides a deterministic description of the dynamic process, which is consistent with the observation data on the relevant scales. As such, it is becoming a common technique for integrated marine environmental monitoring. Because the acoustic propagation contains a wealth of distribution information of the ocean temperature and flow field, the acoustic data provides a new effective data source for data assimilation; in the meantime, acoustic-dynamical data assimilation renders a new technical framework for capturing environmental uncertainty.
Targeting at the impact of the environmental uncertainty on the performance of sonar systems, this thesis focuses on both ocean environmental and acoustic field prediction the-ory and methods research in the framework of the acoustic-dynamical data assimilation. The approach here learned from the Harvard Ocean Prediction System (HOPS) and the Adaptive Rapid Environmental Assessment, and aims to render a train of thoughts for obtaining both sensitive and robust ocean observing information through dynamical mod-eling, dynamical measurements, quantification and fusion, as well as the combination of model and data in the context of real ocean applications. On one hand, acoustic-dynamical data assimilation melds high-resolution environmental measurements and acoustic mea-surements, and models the dynamical system on a relatively small scale required by the acoustic propagation modeling. On the other hand, the acoustic measurement model can be built according to specific system applications, and then coupled to data assimilation results to analyze the error and acoustic measurement performance, which can be used for optimizing the resource deployment of an environmental observational network.
Following the basic framework of the acoustic-dynamical data assimilation, this the-sis has conducted research on the following subjects:oceanographic dynamical modeling, data assimilation algorithm, acoustic measurement modeling, and optimum deployment algorithm for mobile observational systems. The main results can be summarized in three aspects.
1. A modified Garrett-Munk model for the internal wave with the scale on the or-der of100m is introduced as an oceanographic dynamical model based on measurement data from specific shallow water sites relevant to internal wave modeling. The evolution characteristics of sound speed under internal wave perturbations, which is closely related to acoustic measuring process, are then discussed. In the meantime, the implementation framework, function principles, and application methods of the HOPS for large-scale o-cean environmental prediction are described in detail. The prediction results of the HOPS, using the Massachusetts Bay area as an example site, are presented. HOPS prediction re-sults can be used as the initialization and boundary conditions for the acoustic-dynamical data assimilation system.
2. A general framework of the acoustic-dynamical data assimilation is presented, which includes a local sound speed measurement model, an oceanographic dynamical model, a local acoustic pressure measurement model, and an acoustic propagation model. Based on the general framework, three algorithms are developed to estimate the uncertain-ty of sound speed field:the traditional variational approach, the ensemble Kalman filter (EnKF), and the unscented Kalman filter (UKF). For the traditional variational approach, a linear internal wave model is included, and the sound speed is estimated by minimizing the mean square errors of those four models, called the cost function. This method can be applied to nonlinear systems with internal wave disturbances. However, it requires to search for each dimension of the unknown parameters in the possible distribution space, which would result in very high computational complexity for a high dimensional param-eter set. In order to reduce the computational load in data assimilation, the sound speed perturbations are described by the empirical orthogonal functions (EOF), and the general framework of the acoustic-dynamical data assimilation is modeled as a state-space mod-el following the concept of sequential filtering. However, it is difficult to directly derive an explicit, time-varying EOF state equation from an oceanographic dynamical model. Instead, auto-regressive analysis method is introduced here to obtain high-order state evo-lution model of the EOF coefficients based on the oceanographic dynamical model or the sound speed measurements. Compared with the traditional first-order state evolution mod-el, the estimated results of EnKF and UKF based on the high-order state evolution model show better performance.
3. The optimum deployment algorithm for mobile observational systems is conduct-ed for three specific application modes:reducing sound speed field prediction uncertain-ty, reducing acoustic field prediction uncertainty, and improving target localization. The main idea is to create the corresponding objective function, and then minimize the objec- tive function to get the Autonomous Underwater Vehicle path through some very efficient shortest path algorithms. For the first application mode, the objective function is the poste-rior sound speed field uncertainty, which is calculated through the Kalman filter based on the estimated results of the data assimilation implementation. For the second application mode, the objective function is the posterior acoustic field prediction uncertainty. Here, we need to describe the propagation of both environmental and sound speed uncertainty to acoustic field uncertainty. Considering the need for online processing, a linear approxima-tion method is applied to calculate the variance of sound transmission loss, which is defined as the acoustic field prediction uncertainty. For the third application mode, the Cramer-Rao Bound in the Bayesian framework is used to establish acoustic measurement error and cou-pling model, and the uncertainty of environmental parameters strongly coupled with source range is defined as the objective function. The effectiveness of the optimum deployment al-gorithm for different application modes are analyzed via simulations in a realistic shallow water environment selected from the Shelf Break PRIMER experiment.
Finally, experimental data from the SW06Experiment in2006and the Qiandao-Lake experiment in2011are processed to validate some of the theoretical developments. Ex-perimental data processing results have verified the feasibility and effectiveness of the acoustic-dynamical data assimilation for capturing environmental uncertainty. The opti-mum deployment algorithm for mobile observational systems based on reducing sound speed field prediction uncertainty is also validated using the output data from the HOPS running for the region of the Massachusetts Bay.
引文
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