Model-based identification: an adaptive approach to ocean-acousticprocessing

A model-based approach is developed to solve an adaptive ocean-acoustic signal-processing problem. Model-based signal processing is a well-defined methodology enabling the inclusion of propagation models, measurement models, and noise models into sophisticated processing algorithms. Here, we investigate the design of a so-called model-based identifier (MBID) for a general nonlinear state-space structure and apply it to a shallow water ocean-acoustic problem characterized by the normal-mode model. In this problem, we assume that the structure of the model is known and we show how this parameter-adaptive processor can be configured to jointly estimate both the modal functions and the horizontal wave numbers directly from the measured pressure-field and sound speed. We first design the model-based identifier using a model developed from a shallow-water ocean experiment and then apply it to a corresponding set of experimental data demonstrating the feasibility of this approach. It is also shown that one of the benefits of this adaptive approach is a solution to the so-called “mismatch” problem in matched-field processing (MFP)     

Sound velocity profile estimation: a system theoretic approach

A system-theoretic approach is proposed to investigate the feasibility of reconstructing a sound velocity profile (SVP) from acoustical hydrophone measurements. A state-space representation of the normal-mode propagation model is used. It is shown that this representation can be utilized to investigate the so-called observability of the SVP from noisy measurement data. A model-based processor is developed to extract the required information, and it is shown that even in cases where limited SVP information is available, the SVP can be estimated using this approach. Based on this framework, investigations are made of model-based solutions to the sound velocity profile and related parameter estimation problems. In particular, a processor is designed that allows in situ recursive estimation of the sound velocity profile from simulated data     

Internal wave signal processing: a model-based approach

A model-based approach is proposed to solve the oceanic internal wave signal processing problem that is based on state-space representations of the normal-mode vertical velocity and plane wave horizontal velocity propagation models. It is shown that these representations can be utilized to spatially propagate the modal (depth) vertical velocity functions given the basic parameters (wave numbers, Brunt-Vaisala frequency profile, etc.) developed from the solution of the associated boundary value problem as well as the horizontal velocity components. These models are then generalized to the stochastic case where an approximate Gauss-Markov theory applies. The resulting Gauss-Markov representation, in principle, allows the inclusion of stochastic phenomena such as noise and modeling errors in a consistent manner. Based on this framework, investigations are made of model-based solutions to the signal enhancement problem for internal waves. In particular, a processor is designed that allows in situ recursive estimation of the required velocity functions. Finally, it is shown that the associated residual or so-called innovation sequence that ensues from the recursive nature of this formulation can be employed to monitor the model's fit to the data     

Acoustic model-based matched filter processing for fadingtime-dispersive ocean channels: theory and experiment

It is shown that the performance of a conventional matched filter can be improved if the reference (replica) channel compensates for the distortion by the ocean medium. A model-based matched filter is generated by correlating the received signal with a reference channel that consists of the transmitted signal convolved with the impulse response of the medium. The channel impulse responses are predicted with a broadband propagation model using in situ sound speed measured data and archival bottom loss data. The relative performance of conventional and model-based matched filter processing is compared for large time-bandwidth-product linear-frequency-modulated signals propagating in a dispersive waveguide. From ducted propagation measurements conducted in an area west of Sardinia, the model-based matched filter localizes the depths of both the source and receiving array and the range between them. The peak signal-to-noise ratio for the model-based matched filter is always larger than that of the conventional filter     

Model-based enhancement of internal wave images

The measurement of internal-wave signatures using synthetic aperture radar (SAR) or real aperture radar (RAR) techniques is an emerging technology that offers a viable means of locating and tracking surface ship wakes by their unique signatures. Under the assumption that the image measured by the radar is dominated by the underlying dynamics of the internal wave, we develop model-based techniques for enhancement based on a recently developed generic dispersive-wave processor. Using images synthesized by a sophisticated propagation model, it is shown that the processor not only offers a unique approach compared to the more traditional image-processing techniques that do not incorporate the propagation model, but is also capable of providing reasonable enhancement of the noisy measurements     

A hybrid simplex genetic algorithm for estimating geoacousticparameters using matched-field inversion

Matched-fieId inversion (MFI) undertakes to estimate the geometric and geoacoustic parameters in an ocean acoustic scenario by matching acoustic field data recorded at hydrophone array with numerical calculations of the field. The model which provides the best fit to the data is the estimate of the actual experimental scenario. MFI provides a comparatively inexpensive method for estimating ocean bottom parameters over an extensive area. The basic components of the inversion process are a sound propagation model and matching (minimization) algorithm. Since a typical MFI problem requires a large number of computationally intensive sound propagation calculations, both of these components have to be efficient. In this study, a hybrid inversion algorithm which uses a parabolic equation propagation model and combines the downhill simplex algorithm with genetic algorithms is introduced. The algorithm is demonstrated on synthetic range-dependent shallow-water data generated using the parabolic equation propagation model. The performance for estimating the model parameters is compared for realistic signal-to-noise ratios in the synthetic data     

High-frequency ambient noise and its impact on underwater tracking ranges

A theoretical model for the vertical directionality and depth dependence of high frequency (8 to 50 kHz) ambient noise in the deep ocean is developed. The anisotropic noise field at a variety of depths and frequencies is evaluated and displayed. It was found that at high frequencies and deep depths, a bottom-mounted hydrophone receives the maximum noise energy from overhead rather than from the horizontal. This leads to the consideration of an oblate hydrophone receiving response pattern for underwater tracking ranges that would provide a constant signal-to-noise ratio (SNR) for an acoustic source located anywhere in a circular area centered above the hydrophone. Two of the desirable characteristics of this type of pattern are the increase in receiving range of a bottom-mounted sensor and the decrease of the dynamic range of signals that a signal processor must handle.     

Synthetic aperture radar imaging of ocean waves during the marineland experiment

X- andL-band simultaneously obtained synthetic aperture radar (SAR) data of ocean gravity waves collected during the Marineland Experiment were analyzed using wave contrast measurements. The Marineland data collected in 1975 represents a unique historical data set for testing still-evolving theoretical models of the SAR ocean wave imaging process. The wave contrast measurements referred to are direct measurements of the backscatter variation between wave crests and troughs. These modulation depth measurements, which are indicators of wave detectability, were made as a function of: a) the settings used in processing the SAR signal histories to partially account for wave motion; b) wave propagation direction with respect to radar look direction for bothX- andL-band SAR data; c) SAR resolution; and d) number of coherent looks. The contrast measurements indicated that ocean waves imaged by a SAR are most discernible whenX-band frequency is used (as compared toL-band), and when the ocean waves are traveling in the range direction. Ocean waves can be detected by bothX- andL-band SAR, provided that the radar surface resolution is small compared to the ocean wavelength (at least 1/4 of the ocean wavelength is indicated by this work). Finally, wave detection withL-band SAR can be improved by adjusting the focal distance and rotation of the cylindrical telescope in the SAR optical processor to account for wave motion. The latter adjustments are found to be proportional to a value that is near the wave phase velocity.     

Altimeter Signal-to-Noise for Deep Ocean Processes in Operational Systems

The ocean signal for this study is the sea surface height due to the slowly varying (greater than 5-day) ocean processes, which are predominantly the deep ocean mesoscale. These processes are the focus of present assimilation systems for monitoring and predicting ocean circulation due to ocean fronts and eddies and the associated environmental changes that impact real time activities in areas with depths greater than about 200 m. By this definition, signal-to-noise may be estimated directly from altimeter data sets through a crossover point analysis. The RMS variability in crossover differences is due to instrument noise, errors in environmental corrections to the satellite observation, and short time period oceanic variations. The signal-to-noise ratio indicates that shallow areas are typically not well observed due to the high frequency fluctuations. Many deep ocean areas also contain significant high frequency variability such as the subpolar latitudes, which have large atmospheric pressure systems moving through, and these in turn generate large errors in the inverse barometer correction. Understanding the spatial variations of signal to noise is a necessary prerequisite for correct assimilation of the data into operational systems.     

Altimeter Signal-to-Noise for Deep Ocean Processes in Operational Systems

The ocean signal for this study is the sea surface height due to the slowly varying (greater than 5-day) ocean processes, which are predominantly the deep ocean mesoscale. These processes are the focus of present assimilation systems for monitoring and predicting ocean circulation due to ocean fronts and eddies and the associated environmental changes that impact real time activities in areas with depths greater than about 200 m. By this definition, signal-to-noise may be estimated directly from altimeter data sets through a crossover point analysis. The RMS variability in crossover differences is due to instrument noise, errors in environmental corrections to the satellite observation, and short time period oceanic variations. The signal-to-noise ratio indicates that shallow areas are typically not well observed due to the high frequency fluctuations. Many deep ocean areas also contain significant high frequency variability such as the subpolar latitudes, which have large atmospheric pressure systems moving through, and these in turn generate large errors in the inverse barometer correction. Understanding the spatial variations of signal to noise is a necessary prerequisite for correct assimilation of the data into operational systems.     

Bayesian Sonar Detection Performance Prediction With Source Position Uncertainty Using SWellEx-96 Vertical Array Data

Predicting sonar detection performance is important for the development of sonar systems. The classical sonar equation cannot accurately predict sonar detection performance because it does not incorporate the effect of ocean environmental and source position uncertainty. We propose an analytical receiver operating characteristic (ROC) expression that characterizes the performance of the optimal Bayesian detector in the presence of ocean environmental and source position uncertainty. The approach is based on a statistical model of the environment and a physical model of acoustic propagation, which translates ocean environmental and source position uncertainty to signal wavefront uncertainty. The analytical ROC expression developed in this paper is verified for source position uncertainty due to source motion using both simulated data and real data collected during the Shallow Water Evaluation Cell Experiment (SWellEx-96). The results showed that the primary effect of source position uncertainty on optimal sonar detection performance is captured by the rank that corresponds to the significant eigenvalues of the signal matrix, an ensemble of replica signal wavefronts (normalized acoustic pressure vector) at the receiving array. The results also showed that the proposed ROC expression provides a realistic detection performance prediction for the Bayesian detector for source position uncertainty using real data. The proposed approach to sonar detection performance prediction is much simpler and faster than those using conventional Monte Carlo approaches.     

Ocean acoustic field matching: normal mode filtering andnon-Gaussian sources

Two qualitative results concerning statistical sonar signal processing and acoustic field matching are obtained. First, normal-mode field predictions are integrated with statistical signature analysis by constructing a boundary-value problem in the acoustic waveguide. From this construction it is found that the normal-mode filter is the unique acoustic preprocessor which does not confound deterministic waveguide correlation structure with stochastic source covariance structure. Second, the origin of deterministic, Gaussian, and non-Gaussian source signatures is investigated by associating physical parameters with the classical Lindeberg central limit conditions. From construction it is found that there are important objects that are not adequately represented either by infinitesimal points or by infinite surfaces. If receiver resolution is inadequate to resolve source complexity, these objects will exhibit a non-Gaussian acoustic signature via an entirely linear progression from internal excitation, to source radiation, through waveguide propagation, and finally to reception     

A reduced-dynamics variational approach for the assimilation of altimeter data into eddy-resolving ocean models

A new method of assimilating sea surface height (SSH) data into ocean models is introduced and tested. Many features observable by satellite altimetry are approximated by the first baroclinic mode over much of the ocean, especially in the lower (but non-equatorial) and mid latitude regions. Based on this dynamical trait, a reduced-dynamics adjoint technique is developed and implemented with a three-dimensional model using vertical normal mode decomposition. To reduce the complexity of the variational data assimilation problem, the adjoint equations are based on a one-active-layer reduced-gravity model, which approximates the first baroclinic mode, as opposed to the full three-dimensional model equations. The reduced dimensionality of the adjoint model leads to lower computational cost than a traditional variational data assimilation algorithm. The technique is applicable to regions of the ocean where the SSH variability is dominated by the first baroclinic mode. The adjustment of the first baroclinic mode model fields dynamically transfers the SSH information to the deep ocean layers. The technique is developed in a modular fashion that can be readily implemented with many three-dimensional ocean models. For this study, the method is tested with the Navy Coastal Ocean Model (NCOM) configured to simulate the Gulf of Mexico.     

Deterministic three-dimensional analysis of long-range soundpropagation through internal-wave fields

A Munk profile and a set of propagating internal-wave modes are used to construct a three-dimensional time-varying ocean sound-speed model. Three-dimensional ray tracing is employed to simulate long-range sound propagation of a broadband acoustic signal. Methods are developed to convert three-dimensional ray-tracing results to acoustic time-domain amplitude and phase measurements. The ocean sound-speed model is defined deterministically, and the model acoustic receptions are analyzed deterministically. A single internal-wave mode that is “spatially synchronizes” to an arrival can coherently focus and defocus the acoustic energy. These internal waves can cause an arrival's amplitude fluctuation to mimic Rayleigh fading; however, the time-domain phase is stable, in contradiction to the classical Rayleigh fading environment where the received phase is uniformly distributed. For example, the received power attributed to an early arrival propagated over a 750-km range can fluctuate over 40 dB, while the time-domain phase remains within a quarter of a 75 Hz cycle. The characteristics of the time-domain phase are important for establishing coherent integration times at the receiver     

A New Method for Matched Field Localization Based on Two-Hydrophone

The conventional matched field processing (MFP) uses large vertical arrays to locate an underwater acoustic target. However, the use of large vertical arrays increases equipment and computational cost, and causes some problems such as element failures, and array tilting to degrade the localization performance. In this paper, the matched field localization method using two-hydrophone is proposed for underwater acoustic pulse signals with an unknown emitted signal waveform. Using the received signal of hydrophones and the ocean channel pulse response which can be calculated from an acoustic propagation model, the spectral matrix of the emitted signal for different source locations can be estimated by employing the method of frequency domain least squares. The resulting spectral matrix of the emitted signal for every grid region is then multiplied by the ocean channel frequency response matrix to generate the spectral matrix of replica signal. Finally, the matched field localization using two-hydrophone for underwater acoustic pulse signals of an unknown emitted signal waveform can be estimated by comparing the difference between the spectral matrixes of the received signal and the replica signal. The simulated results from a shallow water environment for broadband signals demonstrate the significant localization performance of the proposed method. In addition, the localization accuracy in five different cases are analyzed by the simulation trial, and the results show that the proposed method has a sharp peak and low sidelobes, overcoming the problem of high sidelobes in the conventional MFP due to lack of the number of elements.     

Inertial oscillations as deep ocean response to hurricanes

We discuss the deep ocean response to passing hurricanes (aka typhoons), which are considered as generators of near-inertial, internal waves. The analysis of data collected in the northwestern parts of the Pacific and Atlantic oceans in the hurricane season permit us to assess the deep ocean response to such a strong atmospheric forcing. A large number of moorings (more than 100) in the northwestern Pacific have allowed us to characterize the spatial features of the oceanic response to typhoons and the variable downward velocity of near-inertial wave propagation. The velocity of their downward propagation varies in the range 1–10 m/hour. It is higher in the regions of low stratification and high anticyclonic vorticity. The inertial oscillations generated by a hurricane last for 10–12 days. The mean anticyclonic vorticity in the region increases the effective frequency of inertial oscillations by 0.001–0.004 cyc/hour.     

Monitoring moored instrument motion by optimal estimation

Many oceanographic applications require the positioning of the underwater sensor at measurement times. We consider here the case of subsurface moored tomographic instruments, where the distance between source and receiver must be known within a few meters. For that purpose, a long baseline array is deployed: this system includes a navigator, attached to the mooring element and an array of three transponders set on the ocean bottom. To process the navigation data collected with such system, we have developed a method based on optimal estimation. The triangulation problem is not a basic spherical constraints one and the specificity of deep underwater positioning, related to the variability of the ocean sound speed profile are pointed out. Correcting terms are proposed and introduced into the system. Simultaneous inversion of all data, defining an overconstrained problem allows to estimate biases and errors. The algorithm is applied here to a dataset collected in the Azores-Canary basin during CAMBIOS experiment.     

Towed array shape estimation using Kalman filters-experimentalinvestigations

The problem of estimating the shape of a towed array instrumented with either depth sensors, compasses, or both in a discrete-time state-space formulation is treated in a companion paper by D. A. Gray et al. (to appear), in which the state-space representation is derived from a dynamical model of the propagation of tow-point-induced motion down the array. A Kalman filter is derived to recursively estimate the shape of this towed array, and solutions to the Riccati equation are used to predict the mean square error of the Kalman filter array shape estimates. The present study investigates the performance of this Kalman filter approach as an array shape estimator using both simulated examples and sea trial data. Fundamental to the Kalman filter approach is the model that describes the dynamical behavior of the towed array. The results of an experimental program that was undertaken to validate this model are also presented     

基于PXI-9820对海浪雷达回波采集的设计与实现

针对利用X波段海浪雷达回波图像反演海浪参数(波高、波向、波周期)的需要,而海浪雷达回波信号的采集和存储是利用雷达回波图像序列反演海浪参数的必要前提,提出了基于PXI-9280 A/D采集卡对X波段海浪雷达回波进行采集的方案.具体介绍了PXI-9280 A/D采集卡的功能特性,海浪雷达回波信号样式,设计并完成了基于VC 6.0的采集和存储软件,并对使用该采集卡需要注意的实际问题给予说明.此外,还搭建了实验平台,给出了基于PXI-9280 A/D采集卡在我国南海某海域中采集数据并回放得到的海浪雷达回波图像,得到较满意的结果.     

PDO的三维空间结构和时间演变特征

利用再分析次表层海温资料和CCSM3模式的1870-1999年130 a模拟试验的结果,分析了北太平洋年代际变化(PDO)的三维空间结构和时间演变特征.结果表明,CCSM3模式较好的模拟了北太平洋年代际变化的主要特征,对再分析资料和模式结果的分析都表明从北太平洋表层至次表层的中层,年代际变化是非常显著的,PDO不仅仅局...