海洋环境噪声观测技术及数据处理方法

海洋环境噪声是水声信道中的干扰背景场,是影响主动声纳探测能力、限制被动声纳工作性能发挥的主要原因之一.文中详细介绍了目前海洋环境噪声观测的技术手段、测量方法和数据处理方法,并根据实际测量的海洋环境噪声数据,详细分析了海洋环境噪声1/3倍频程带宽内的噪声频谱级计算方法和步骤.     

海洋-声学-探测模型的并行预报技术研究

针对声纳探测性能预报的需求,建立了海洋-声学-探测模型,实现了并行计算。该模型将海洋数值预报模式、声学模式与海洋观测资源进行整合,辅以战术需求,实现了对水声环境和声纳探测性能的动态预报和估计。同时,结合高性能计算机系统,设计并实现了模型的并行算法,结果表明,该程序具有较高的并行性能,较好地满足了声纳探测性能预报对实效性的要求,为形成业务化海洋水声环境预报能力打下了良好的基础。     

基于AUV的声纳水下目标实时探测机制研究

针对传统船载声纳探测水下目标存在成像分辨率低、主观性强、耗时长、应用区域局限,以及自主式水下潜航器(autonomous underwater vehicle, AUV)受水声通信限制导致数据无法实时回传、处理及目标实时探测的问题,提出了一种基于AUV的声纳水下目标实时探测机制。首先对基于AUV搭载声纳设备实施水下目标探测的系统进行了阐述;然后提出了基于AUV的声纳水下目标实时探测实施流程和关键技术;最后通过海上试验,验证了该机制在一定程度上克服了水声通信限制,实现实时、高效、智能的水下目标探测,具有较强的实际指导意义。     

潜艇声纳系统水声信号模拟

声模仪供训练声纳员使用。在声模仪里水声信号模拟的依据是水声信号检测理论,在对舰船辐射的噪声和背景干扰噪声及潜艇声纳系统检测信号方法分析的基础上,采用电子线路对水声信号进行模拟。     

海洋声学技术在无缆水下机器人中的应用

1 概述 电磁波在水中衰减严重,传播距离较短。声波可以在海洋中传播很远的距离,声波是海洋中的主要信息载体。海洋声学技术在海洋中用于探测、通信、导航和定位,它是海洋遥感技术的重要组成部分,近年来获得迅速发展。 海洋声学技术主要包括三大类声纳,即探测声纳、导航声纳和水声通信机,详见附表Ⅰ。     

实孔径成像声纳在海底油气管线探测中的应用

利用成像声纳对油气管线进行探测,是重要的声学测量技术手段之一。侧扫声纳和环扫声纳是两种典型的实孔径成像声纳,介绍了这两种声纳的成像特点,分别从搭载平台、扫测特点、参数显示和声图特征等4个方面进行了比较,结合工程实例给出了两种声纳对海底油气管线探测的应用效果,分析比较了二者的优缺点。所得结论对于了解和掌握侧扫声纳和环扫声纳设备性能,进行海底油气管线检测和状态评估具有重要的工程实用意义。     

基于虚警函数的侧扫声纳水下目标实时检测方法

研究了侧扫声纳系统进行水下目标探测过程中目标信号的检测问题。通过分析海底回波信号的统计模型及其参数的估计,讨论了目标信号对统计模型拟合的影响规律,提出了侧扫声纳回波信号虚警函数和虚警率的概念,及其对Ping信号中目标信号的检测方法。算例结果表明,回波信号的三种分布模型中K分布拟合程度最优,在相同虚警率的条件下,基于K分布的虚警函数目标检测率最高。该法可为侧扫声纳回波信号中目标的实时报警提供技术支撑。     

侧扫声纳在护底工程软体排检测中的应用

介绍了Edge Tech 4100-P型侧扫声纳系统的技术指标和工作原理。根据水下潜堤护底工程软体排的铺排特点,侧扫声纳系统可以高效地探测出海底面以上相邻的软体排结构物的拼接和叠加情况,同时还可以根据侧扫声纳图像判断软体排有无破损及破损后填充物流失情况,评估水下潜堤护底工程的安全系数。通过实例说明了侧扫声纳在软体排检测的外业工作流程,以及内业图像识别与分析,探讨侧扫声纳系统在水下水工结构物探测的应用前景。     

AUV定位信号检测延时的蒙特卡洛模拟分析

在简要介绍AUV声学定位声纳接收机原理基础上,分析了CW脉冲信号在极性相关检测电路中的传输过程,建立了极性相关积分检测延时仿真分析模型。提出采用蒙特卡洛模拟方法获取检测延时的分布特征和统计参数的观点。实验结果表明蒙特卡洛模拟实验与硬件电路实验结果一致,对于解决随机性检测延时问题具有很强的能力。获得的结果可为AUV定位声纳检测门限的设定、声学测距和定位精度分析以及水声通信延时分析提供参考。     

侧扫声纳系统在海底障碍物扫测中的应用

针对海道测量中侧扫声纳位置精度低的问题,利用GPS定位数据、单波束水深数据和侧扫数据,研究侧扫声纳扫海测量中海底障碍物信息精化问题。海上试验数据表明：文中提出的多源水声测量信息联合扫测海底障碍物的方法,可改善单一侧扫系统目标探测的精度,对海底目标物扫测具有较好的适用性。     

基于回波时间的多波束测深与声纳数据匹配方法

以多波束精确的水深数据为参照源,采用原始回波时间对多波束测深数据与其同源声纳数据进行匹配,从而获得高精度和高分辨率的海底影像数据,并避免了传统声纳图像处理过程中斜距改正所带来的几何形变。匹配结果采用光照图输出,并与三维水深图、原始声纳图像和CARIS处理后的声纳图像进行比较分析。该方法有效地提高了多波束数据的利用率,增强了对海底地形的探测分辨率。     

A Statistical Analysis of the Detection Performance of a Broadband Splitbeam Passive Sonar

An operational passive sonar is required to detect signals from sources, which are subject to spatial and temporal coherence losses via modifications by the ocean environment. Furthermore, these signals are to be detected in the presence of frequency-dependent correlated noise fields. For a system which employs splitbeam cross-correlation processing, the spatial and spectral properties of the signal and noise are of significant import. Therefore, the exact probability density and cumulative distribution functions of the N-sampled correlator outputs of a splitbeam broadband passive sonar are derived for the case of Gaussian inputs which are described by arbitrary cross-spectral density matrices. The validity of approximating the exact probability density function (pdf) as a Gaussian distribution is investigated. The effect of signal coherence loss and noise correlation on the detection performance is considered and the associated processing loss is expressed as a degradation factor within the detection threshold equation     

Pulse-length-tolerant features and detectors for sector-scan sonar imagery

This paper presents a neural-network-based system to detect small man-made objects in sequences of sector-scan sonar images created using signals of various pulse lengths. The detection of such objects is considered out to ranges of 150 m by using an experimental sector-scan sonar system mounted on a vessel. The sonar system considered in this investigation has three modes of operation to create images over ranges of 200, 400, and 800 m from the vessel using acoustic pulses of a different duration for each mode. After an initial cleaning operation performed by compensating for the motion of the vessel, the imagery is segmented to extract objects for analysis. A set of 31 features extracted from each object is examined. These features consist of basic object size and contrast features, shape moment-based features, moment invariants, and features extracted from the second-order histogram of each object. Optimal sets of 15 features are then selected for each mode and over all modes using sequential forward selection (SFS) and sequential backward selection (SBS). These features are then used to train neural networks to detect man-made objects in each sonar mode. By the addition of a feature describing the sonar's mode of operation, a neural network is trained to detect man-made objects in any of the three sonar modes. The multimode detector is shown to perform very well when compared with detectors trained specifically for each sonar mode setting. The proposed detector is also shown to perform well when compared to a number of statistical detectors based on the same set of features. The proposed detector achieves a 92.4% probability of detection at a mean false-alarm rate of 10 per image, averaged over all sonar mode settings.     

高分辨率多波束声纳系统海底目标物检测技术

结合近几年出现的高分辨率多波束测深系统的性能特点,通过检测实例,介绍其在海底管道(尤其是裸露管道)检测中的相关技术及及检测成果。结果证实,高分辨率多波束测深系统可以准确检测海底小尺寸目标物,为其新特性的推广应用提供技术参考。     

探猎雷装备探测概率分类方法研究

探猎雷装备作为海军反水雷部队的主要装备，其探测应用研究对提高建制式反水雷装备的作战效能具有重要意义。从作战部队探猎雷实战使用流程出发，结合探猎雷装备应用现状，提出了声呐探测目标的概率分类方法，建立了探测概率分类方法模型，对实际应用需解决的问题进行了探析。对开展海区水雷目标数量评估、目标的识别比对以及声呐探测航次优化等装备作战运用有一定的启发和促进作用。     

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.     

Estimation of Transmission Loss in the Presence of Geoacoustic Inversion Uncertainty

A common problem in sonar system prediction is that the ocean environment is not well known. Utilizing probabilistic based results from geoacoustic inversions we characterize parameters relevant to sonar performance. This paper describes the estimation of transmission loss and its statistical properties based on posterior parameter probabilities obtained from inversion of ocean acoustic array data. This problem is solved by first finding an ensemble of relevant environmental model parameters and the associated posterior probability using a likelihood based inversion of the acoustic array data. In a second step, each realization of these model parameters is weighted with their posterior probability to map into the transmission loss domain. This approach is illustrated using vertical-array data from a recent benchmark data set and from data acquired during the Asian Seas International Acoustics Experiment (ASIAEX) 2001 in the East China Sea. The environmental parameters are first estimated using a probabilistic-based geoacoustic inversion technique. Based on the posterior probability that each of these environmental models fits the ocean acoustic array data, each model is mapped into transmission loss. This enables us to compute a full probability distribution for the transmission loss at selected frequencies, ranges, and depths, which potentially could be used for sonar performance prediction.     

基于小波变换的声纳图像边缘特征检测研究

声纳图像的边缘特征检测是其目标识别技术的重要技术基础。声纳图像背景复杂、噪声污染严重,而传统的边缘检测方法对图像噪声非常敏感,所以针对这一特点,利用小波变换易于消除噪声、运算方便的数学特征,提出了一种基于小波变换的声纳图像边缘特征检测算法。由计算机仿真结果可以得到,与传统的边缘检测算法相比,此算法在有效地抑制噪声的同时,还可以得到较高的边缘定位精度,能够很好地检测到原始声纳图像的边缘。     

A New Probability of Detection Model for Updating Crack Distribution of Offshore Structures

1 .IntroductionNondestructiveinspection (NDI)isveryimportantforensuringthereliabilityofoffshorestructuresintheirservicelives (Lauraetal.,1 996 ) .Itiswellknownthatdetectionofflawsinvolvesconsider ablestatisticaluncertainties.Asaresult,theprobabilityofdetection (POD)forallflawsofagivensizehasbeenusedintheliteraturetodefinethecapabilityofaparticularNDItechniqueinagivenen vironment.SincethedataofPODusuallyscatterlargely ,itisdifficulttodeterminewhichmodelfitstheavailabledatabest.Thismodelun…