水下声信号红外激光探测的光线分析

水下声信号在水-空气界面会引起表面波动而对打在水表面的激光进行调制。利用直接光强检测方法可以检测到受水下声信号调制的激光信号,本文用光线分析理论模型证明了利用激光进行水下声波探测的可行性。     

激光探测水下声信号的实验研究

在实验室条件下,对于不同频率、不同振动强度的水下声音信号展开激光相干探测研究,建立了基于该方法的实验系统。水表面在水下声音信号作用下产生波动时,用激光照射水表面,其产生的水表面散射光携带了声波信息并与参考光发生干涉,对干涉信号进行采集并处理可得到水下声信号的频率与强度信息。对不同条件下得到的实验结果进行对比分析。实验结果表明,激光相干探测技术可有效地探测水下声信号,并且随着声信号的频率提高、强度减弱,探测效果趋于变差。实验系统采用全光纤光路设计,取得了较好的效果。     

利用激光探测水下声信号的研究

对激光探测水下声信号可行性进行了研究.水下声信号造成的水表面微扰导致了反射的激光光通量的变化,探测器接收到的光通量的变化频率与水下声信号的频率相同,并给出了探测器位置与接收光通量的关系.     

水下声信号激光相干探测的仿真与实验

建立波长1 550 nm的全光纤激光相干探测系统,系统采用全光纤设计,光路简单且稳定性高.数值仿真与实验结果表明,采用激光相干探测和时-频分析,可有效地提取水下不同频率、不同强度和不同深度的振动特征.该系统可实时探测出40~10 000 Hz的水下声信号,且测量标准偏差小于几个赫兹.因此,激光相干雷达用于水下目标的探测与识别具有实时性,该技术可为水下目标的特征提取和识别提供新的途径.     

水下声信号激光探测技术研究

水下声信号激光探测技术采用了激光接收技术。它在空气中利用光波,而在水中利用声波,把两种最佳的信道和物理场结合了起来,是遥感探测水下声信号的一种比较理想的方法。水下声信号在水空气界面会引起表面波动而对打在水表面处的激光束进行幅度调制。利用直接光强检测方法可以检测受水下声信号调制的激光信号。本文在理论分析的基础上通过试验验证了激光探测水下声信号技术的可行性,同时对水下声信号光电探测存在的问题进行了探讨并提出了相应的解决途径。     

中低频水下声信号的激光干涉法探测

基于迈克尔逊干涉仪原理,提出了在非平静水面情况下探测中低频水下声信号的激光干涉方法。利用携带声波信息的散射光和参考光的干涉结果提取水下声信号的频率信息,并进行了理论分析和实验验证,结果表明,激光干涉法可以准确探测出中低频水下声信号的频率信息。     

水下声场激光相干探测的实验研究

设计了一套光纤光路探测水下声场的实验系统, 对水下振动进行相干探测。实验结果和特征提取分析表明该系统可对水下振源在水表面激起的横向微波的频率进行实时、准确的监测; 频谱分析和时-频域联合分析结果显示该系统能够实时探测10 Hz到20 kHz的水下声信号。     

基于Morlet小波的水下声信号频率识别

为准确获取水下目标的发声频率,建立了激光干涉法探测水下声信号的实验系统。提出一种基于Morlet小波的水声信号处理方法,将Morlet小波母函数的频率取定值,改变尺度因子,利用两者比值与水下声信号频率的关系分析小波系数模值,实时获取水下声信号的频率信息。实验结果表明:小波系数图可以反映出水下目标在某一时刻的发声频率,实验系统能够实时探测出1～15kHz的水下声信号。     

一种水下声信号激光遥感探测系统

论述了水下声信号激光探测的原理及系统结构.具体设计了光探测器、前置放大电路、高通滤波电路、后级放大电路、低通滤波电路、50 Hz陷波器电路、串口通信电路、存储器扩展电路以及基于数字信号处理器TMS320LF2407A设计了控制和运算电路,并利用虚拟仪器来实现探测信号的实时显示.实验证明,该系统能够较好地完成对水下声信号的探测和数据处理.     

Vertical and horizontal synchronization services with outlier detection in underwater acoustic networks

Underwater Acoustic Networks (UANs) have important applications in ocean exploration and lake pollution monitoring. UANs are however different from terrestrial sensor networks due to their highly variable, long propagation delay, and mobility. Clock synchronization is an important protocol to achieve timing‐based sensor communications. In this paper, we propose a three dimensional, scalable UAN time synchronization scheme that can achieve both horizontal (i.e., in the same water depth) and vertical (i.e., from bottom up to the surface) clock synchronization to overcome the effects of long acoustic delay. To secure UAN clock synchronization services, we also propose a two‐step security UAN synchronization model: (1) correlation test and (2) statistical reputation and trust model. The proposed model can detect outlier timestamp data and identify nodes generating insider attacks. Copyright © 2007 John Wiley & Sons, Ltd.     

基于支持向量机的水声信号多分类器设计

目标分类器是水下目标自动识别系统的重要组成部分,目前水下目标分类的方法主要有统计分类、神经网络和专家系统等三大类的分类方法。支持向量机（SVM,Support Vector Machine）是根据统计理论提出的一种新的算法,该算法具有良好的泛化性能,不仅对训练样本的分类性能较好,对未知的检验样本同样具有好的分类效果,特别适用于小样本数据的分类。本文将该算法推广至多分类情况,并对三类水声信号样本进行分类试验。实验结果表明,该算法可以有效的避免“维数灾难”问题,且分类正确率高于传统的神经网络分类器。     

一种水声信号的模拟研究和检测

为了实现对水下目标的定位,提出一种CW信号与LFM信号组合的水声信号机制,并完成了模拟检测。该信号机制中CW信号用于测距,LFM信号用于测深。一组帧信号与三组行信号组合,共同完成目标定位,并且有效减小误差。仿真结果表明,该信号机制在水声信道中能有效的传输,被检测及实现定位要求。     

Oceanic forces and their impact on the performance of mobile underwater acoustic sensor networks

This paper focuses on the performance analysis of Underwater Wireless Acoustic Sensor Networks (UWASNs) with passively mobile sensor nodes moving because of the influence of major oceanic forces. In an UWASN, passive node mobility is inevitable. Therefore, the performance analysis of UWASNs renders meaningful insights with the inclusion of a mobility model, which represents realistic oceanic scenarios. In this regard, the existing works on performance analysis of UWASNs lack the consideration of major dominating forces, which offer impetus for a node's mobility. Additionally, the existing works are limited to only shallow depths and coastal areas. Therefore, in this paper, we have proposed a physical mobility model, named Oceanic Forces Mobility Model , by incorporating important realistic oceanic forces imparted on nodes. The proposed model considers the effects of node mobility in 3‐D space of water. We also present an analysis on the impact of node mobility on the performance of UWASNs in terms of network dispersion and localization. Simulation results indicate performance degradation of UWASNs in the presence of oceanic forces—localization coverage decreases by 36.70% , localization error increases nearly by 21.14% , and average energy consumption increases by 3% approximately. Copyright © 2014 John Wiley & Sons, Ltd.     

船载式水声跟踪系统定位原理及误差分析

阐述了水下定位系统的研究意义和基本分类,介绍了船载式水声跟踪系统的定位原理、实现方法,以及优点和不足之处,给出定位计算及姿态修正公式。针对声速、基阵孔径、时延值和姿态角等参数对系统产生的误差,进行理论公式推导和分析,最后给出系统误差公式。结合误差公式和随机误差理论,分析了跟踪系统的定位误差来源,并给出减小误差的基本方法。     

一种基于有限状态机的新型水声网络多址接入协议的实现

本文首先提出了一种新型的水声通信网络的多址接入(UAMA)协议.该协议针对水声信道传播时延长的特点,利用扩频码进行多信道预约;通过适当的控制方法,采用与传统的握手协议不同的机制,当发送方发送request_to_send(RTS)后,在选择的业务信道上立刻传输数据,而不必等收到clear_to_send(CTS)后再传输数据,在时间和空间上大大地提高了信道的利用率,因此极大地提高了网络的吞吐量.通过使用OPNET Modeler/Radio仿真器对UAMA协议进行了基于有限状态机(FSM)的建模和设计.仿真结果表明该协议能在水声环境下取得优良的吞吐性能和较低的分组传输时延.     

A survey on deployment techniques,localization algorithms,and research challenges for underwater acoustic sensor networks

In recent years, wireless sensor networks (WSNs) have attracted the attention of both the research community and the industry, and this has eventually lead to the widespread use of WSNs in various applications. The significant advancements in WSNs and the advantages brought by WSNs have also enabled the rapid development of underwater acoustic sensor networks (UASNs). In UASNs, in addition to deployment, determining the locations of underwater sensor nodes after they have been deployed is important since it plays a critical role in many applications. Various localization techniques have been proposed for UASNs, and each one is suitable for specific scenarios and has unique challenges. In this paper, after presenting an overview of potential UASN applications, a survey of the deployment techniques and localization algorithms for UASNs has been presented based on their major advantages and disadvantages. Finally, research challenges and open research issues of UASNs have been discussed to provide an insight into future research opportunities.     

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

Nowadays, multiple input multiple outputs with orthogonal frequency division multiplexing (MIMO-OFDM) provide better communication performance that can be applied to the fast-growing Internet of Things (IoTs). In underwater IoT, information fades away rapidly due to varying water conditions. Therefore, the MIMO model can be applied to the OFDM acoustic system, enabling high-speed data transmission without affecting the channel effectively. However, detecting the underwater signal and estimating the channel is highly necessary for enhancing underwater acoustic communication (UWAC). Recently, many techniques have been introduced for effectively performing signal detection and channel estimation. However, those techniques face high time complexity due to increased channel interferences and noises during data transmission. Hence, this article brings a novel technique for SD and CE for the UWAC-IoT-enabled MIMO-OFDM system. An adaptive recursive least square (ARLS) technique is proposed in this study for CE that aids in evaluating the acoustic channel parameters effectively. For performing SD, a bi-directional deep pelican convolutional neural network (BDPCNN) technique is introduced to ensure the presence and absence of signals at the receiver end. The proposed method is analyzed via the MATLAB platform, and the performances are analyzed under different water types like turbid water, coastal water, clear ocean water, and pure seawater. Different performance metrics like bit error rate (BER), mean square error (MSE), energy efficiency (EE), and time complexity are analyzed with different existing techniques. The experimental section obtains the BER of 0.0086, 0.013, 0.017, and 0.021 for turbid, coastal, clear, and pure seawater, respectively.