基于粒子群优化算法的PSO-BP海底声学底质分类方法

利用粒子群优化算法（PSO）较强的鲁棒性和全局搜索能力等优点,将PSO算法与BP神经网络相结合,优化了BP神经网络分类时的初始权值和阈值。基于珠江河口三角洲的侧扫声呐图像数据,提取了海底声呐图像中砂、礁石、泥3类典型底质的6种主要特征向量,利用PSO-BP方法对海底底质进行分类识别。实验表明,3类底质分类精度均大于90%,高于BP神经网络70%左右的分类精度,表明PSO-BP方法可有效应用于海底底质的分类识别。     

基于声场模型的猎雷声呐探测成像仿真方法

针对猎雷声呐对水雷目标探测仿真问题,提出一种基于运动学信息与水下声场传播耦合分析的探测成像仿真方法。利用Bellhop3D声场分析方法对水下声信道信号冲击响应进行计算,结合信号复分析方法得到信号传播信道参数以构建声散射模型,以运动耦合方式综合分析声呐搭载平台位置、姿态及速度等因素对回波信号的影响,通过综合考虑上述因素来模拟目标回波信号,从而利用较为真实的等效回波信号进行图像重构。以高频前视声呐为例,对声呐探测沉底水雷目标情况进行了仿真,结果表明,该方法能够得到高频声呐对沉底水雷目标的探测图像,与实际情况具有一致性,可为进一步构建反水雷相关模拟仿真训练系统提供参考。     

西北太平洋水声环境区划及声传播特征

基于2006年Argo资料,计算两北太平洋海域0-1 500 m声速剖面.针对声速结构分类中分类数目难以客观选取和分类结果易陷入局部最优等问题,采用聚类中心动态调整的遗传编码方案和操作算子,充分利用模糊聚类与遗传算法的优势,运用改进的遗传聚类算法对西北太平洋声速剖面进行分类区划基于声场环境区划结果,运用Kraken简正波传播模型(Kraken Normal Wave Propagation Model)模拟典型声速结构的声传播损失场,借助表征声呐效能的优质因数(FO)M),分析典型声速结构的传播损失特征及其对声呐探测、水下潜器等活动的影响     

基于小波分析的侧扫声呐信号非线性增强算法

侧扫声呐回波信号是形成侧扫声呐图像的基础,是侧扫声呐系统对水下目标的最直接观测量,将一维小波变换与非线性增强方法相结合,提出了一种基于小波变换的侧扫声呐回波信号非线性增强算法,用以改善侧扫声呐图像对比度低、噪声强度大的问题.首先利用改进的Bayes阈值对侧扫声呐ping信号进行一维小波分解,提取信号特征信息;然后利用2...     

基于声呐图像的深海采矿车组合定位导航算法

实现高精度的定位导航是深海采矿车完成海底工作任务的基础条件。在采矿车行进过程中,声呐设备生成的图像信息能够反映海底场景的变化,从而体现采矿车本身的运动,由此建立了一种声呐图像里程计,并将其与轮式里程计和USBL测量数据相结合提出了一种深海采矿车组合定位导航算法。首先对多波束前视声呐图像进行预处理,然后使用Canny算法进行特征检测并对特征点云进行配准,再结合声呐成像原理构建了声呐图像里程计运动模型,最后通过轮式里程计运动模型推导预测方程、声呐图像里程计运动模型和USBL测量数据推导更新方程,利用EKF（extended Kalman filter）算法实现基于多传感器融合的定位与姿态估计。海试数据验证了该组合定位算法能实现轮式里程计、声呐里程计和超短基线在速度、位置、艏向角估计、定位速率的精度互补,具有一定的有效性和精确性,该算法为深海采矿车的定位与导航算法研发提供了参考。     

收发分置声纳浅海近程混响信号建模研究

研究了收发分置声纳浅海近程混响的建模与仿真,模型主要基于单元散射理论,依据散射系数相关半径来划分海面、海底散射单元,通过模拟海面、海底混响形成的物理过程建立单接收与多接收模型。模型中考虑声纳设备参数(指向性、收发位置、发射信号)及环境因素(海面运动、海底粗糙程度)对混响建模的影响。设计程序实现浅海近程单接收与多接收混响信号模型并仿真计算出混响时间序列,提供GUI(Graphical User Interface)用户图形界面支持。对建模仿真的混响信号进行统计分析,验证了论文建立的浅海混响信号模型的正确性。     

青岛日偏食对GPS定位精度的影响

2009年7月22日日全食期间,在青岛采用固定点监测单频GPS信号,同时利用非日食期的单频GPS信号监测数据统计对比分析认为,青岛发生的日偏食现象,对GPS单频接收系统的导航定位精度有影响.表现为误差有所增大,接收卫星数减少,PDOP值增大.短时位置偏差最大值为4.02m,历时2min 48s.但青岛地区日偏食对GPS...     

UUV平台声兼容问题研究

UUV平台上声呐设备集成度高,设备之间容易相互干扰产生声兼容问题.通过理论与实例,针对UUV平台上2类容易被忽略的声兼容问题——强信号干扰弱信号和谐波干扰,对其产生机理进行了分析,并据此提出了一套UUV平台声兼容的系统设计方案.研究可以为UUV平台的声兼容设计与分析解决实际工程应用中遇到的声兼容问题提供参考.     

声呐,鲸类的天敌?

正"声呐太强,我屎都喷出来了!"这是《美人鱼》里邓超饰演的地产开发商在亲身体验公司研制的声呐功率后得出的结论。人鱼族因为强功率的声呐而遭受了灭顶之灾。那么,声呐到底对海洋生物有多大的影响呢?声呐系统使用声能来刻画海面下物体的物理性质并且对其定位。声呐应用非常广泛。按照频率分类,可以分为低频声呐、中频声呐和高频声呐;按照工作原理,也可以分为主动声呐和被动声呐。主动声呐主动发射声波"照射"目标,而后接收水中     

声呐系统中海洋生物叫声应用效能分析

发射信号波形的选择是主动声呐设计过程中必须要考虑的问题之一,不同的信号形式会直接影响声呐的性能。海洋哺乳生物通过叫声实现种群间的通信以及对水下环境的感知,这一常见的生物学行为激发了基于生物叫声的声呐系统的开发。但海洋生物在全球分布广泛并且叫声种类繁多,需要结合声呐系统的设计参数对采集到的叫声进行效能分析,才能在不改变传统声呐设计的基础上拓展仿生隐蔽探测功能。针对这一问题,提出了基于主动声呐信号分析的海洋生物叫声效能分析方案。以采集到的 6 种海豚叫声为例,基于同步压缩变换和贪婪算法重构样本信号,从 5 个维度对声呐系统仿生信号进行了分析。仿真结果表明,该效能评估方案可对海洋生物叫声进行有效筛选,以确定适合声呐系统的信号波形。     

Transient sonar signal classification using hidden Markov modelsand neural nets

In ocean surveillance, a number of different types of transient signals are observed. These sonar signals are waveforms in one dimension (1-D). The hidden Markov model (HMM) is well suited to classification of 1-D signals such as speech. In HMM methodology, the signal is divided into a sequence of frames, and each frame is represented by a feature vector. This sequence of feature vectors is then modeled by one HMM. Thus, the HMM methodology is highly suitable for classifying the patterns that are made of concatenated sequences of micro patterns. The sonar transient signals often display an evolutionary pattern over the time scale. Following this intuition, the application of HMM's to sonar transient classification is proposed and discussed in this paper. Toward this goal, three different feature vectors based on an autoregressive (AR) model, Fourier power spectra, and wavelet transforms are considered in our work. In our implementation, one HMM is developed for each class of signals. During testing, the signal to be recognized is matched against all models. The best matched model identifies the signal class. The neural net (NN) classifier has been successfully used previously for sonar transient classification. The same set of features as mentioned above is then used with a multilayer perceptron NN classifier. Some experimental results using “DARPA standard data set I” with HMM and MLP-NN classification schemes are presented. A combined NN/HMM classifier is proposed, and its performance is evaluated with respect to individual classifiers     

A neural network based hybrid system for detection,characterization, and classification of short-duration oceanic signals

A comprehensive classifier system is presented for short-duration oceanic signals obtained from passive sonar, which exhibit variability in both temporal and spectral characteristics even in signals obtained from the same source. Wavelet-based feature extractors are shown to be superior to the more commonly used autoregressive coefficients and power spectral coefficients for describing these signals. A variety of static neural network classifiers are evaluated and are shown to compare favorably with traditional statistical techniques for signal classification. The focus is on those networks that are able to time-out irrelevant input features and are less susceptible to noisy inputs, and two new neural-network-based classifiers are introduced. Methods for combining the outputs of several classifiers to yield a more accurate labeling are proposed and evaluated. These methods lead to higher classification accuracy and provide a mechanism for recognizing deviant signals and false alarms. Performance results are given for signals in the DARPA standard data set I     

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。     

Cramer-Rao lower bounds for sonar broad-band modulation parameters

A role of passive sonar signal processing is the detection and estimation of the parameters associated with amplitude modulated broad-band signals. An example of such signals is propeller noise. Discrete frequency lines occur at the rotational frequency of the propulsion shaft and at the blade frequency. This correspondence provides expressions for the Cramer-Rao lower bounds for the estimates of broad-band signal power, modulation level, modulation frequency, and modulation phase. It is shown that for low broad-band-signal-to-broad-band-noise ratios, the estimates of power and modulation level are uncoupled from the estimates of modulation frequency and phase     

Classification of transient sonar sounds using perceptually motivated features

This paper describes a novel framework for classifying underwater transient signals recorded by passive sonar. The proposed approach involves two key ideas. Firstly, a feature-selection algorithm is used to identify those acoustic features that optimally model each class of transient sound. Secondly, features that are perceptually motivated are proposed, i.e., they encode information that human listeners are likely to use in transient classification tasks. Three perceptual features are proposed, which encode timbre, the physical material of the sound source, and the temporal context (pattern) in which the transient occurred. The authors show how these features, which are computed over different temporal windows, can be combined to make classification decisions. The performance of the proposed classifier is evaluated on a corpus of transient signals extracted from passive sonar recordings. Specifically, the performance of the perceptual features is compared with spectral features and with those that encode statistics of time, frequency, and power. The present results show that the perceptual features provide valuable cues to the class of a transient. However, the best performing classifier was obtained by selecting a subset of perceptual, spectral, and statistical features in a class-dependent manner.     

Searching for seafloor massive sulfides: a quantitative review of high-resolution methods in deep sea sonar bathymetry for mining applications

Seafloor massive sulphides are deep sea mineral deposits currently being examined as a potential mining resource. Conventional sonar bathymetry products gathered by sea surface platforms do not achieve adequate spatial resolution to detect these resources. High-resolution beamforming methods (such as multiple signal classification and estimation of signal parameters via rotational invariance techniques) improve the resolution of sonar bathymetry. We perform a quantitative review of these high-resolution methods using a novel simulator, showing results in the absence of platform motion for a single ping cycle. It was found that high-resolution methods achieved greater bathymetric accuracy and higher resolution than conventional beamforming and that these methods may be adequate for this style of marine exploration. These methods were also robust in the presence of unwanted persistent signals and low signal to noise ratios.     

A method minimizing the correlation properties of a noise field andimproving detection of signals embedded in anisotropic noise

This paper deals with the development of a processing technique that improves the signal-to-noise ratio (SNR) at the single sensor for a received signal that is embedded in a partially correlated noise field. The approach of this study is unique in that the noise is treated as being non-white and partially correlated. The concept of the proposed development is based on the time interval over which the temporal coherence or correlation properties of a noise field are defined. For narrowband signals, the associated temporal coherence period is much longer than the correlation time interval of the anisotropic noise field. Thus, a coherent integration of discontinuous segments of received signals will enhance the SNR at the single sensor by lowering the correlation properties of the associated non-white noise. Reconstruction of the narrow-band signal time series, with improved SNR at the sensor will allow the use of the existing high resolution techniques to be utilized more effectively by lowering their threshold values in order to detect very weak signals. The intention here is to integrate the characteristics of the real anisotropic noise field during the preliminary processing stages of the received signals by an array of sensors. Simulations show that the proposed method can be integrated in the signal processing functionality of sonar and radar systems     

一种多波束声呐回波信号时延的实时特征窗分析方法

本文介绍了一种多波束声呐回波信号时延的实时分析方法。通过实时提取信号的幅度及形状的综合特征计算出信号的时延。该方法具有较强的抗干扰能力,算法快速、准确,适合于多波束声呐系统的回波信号实时分析处理。     

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