基于水下传感器网络的目标识别研究

摘 要I摘 要随着科学技术的发展，以及国防安全的需要，对于水下目标的识别已经变得越来越重要。水下目标识别是水声装备发展的三项关键技术（探测、定位、识别）之一，是探测系统智能化的重要标志，同时也是声纳信息理论中急待解决的难题。开展该领域的研究具有极其重要的现实意义与军事价值。水下目标识别分为主动识别和被动识别两种，本文研究的是被动识别技术。它是将被动声纳接收的水下目标噪声信号先进行特征提取，提取出能够反映目标特征的特征向量，然后设计一个目标分类器，最后将提取出的能够反映目标本质特性的特征向量送入目标分类器进行分类识别。在特征提取阶段，本文将采集的水下目标的信号进行快速傅里叶变换（FFT）得到信号的功率谱，然后对功率谱进行特征提取，其中最主要的特征提取方法包括连续谱特征提取、线谱特征提取、调制连续谱特征提取、调制线谱特征提取，这样就可以得到信号的基于不同特征提取方法的特征向量。在得到目标的特征向量后，首先设计一个自适应遗传 BP 神经网络分类器对目标进行分类处理，经仿真实验表明该特征分类器能够有效地对水下目标信号进行识别，其识别率达到了 85%以上。为了体现基于水下传感器网络的目标识别，本文又采用了基于 D-S 证据理论的方法对目标进行融合识别，其过程为：首先训练一个 BP 神经网络，然后把上文所介绍的水下目标信号的各个特征向量输入训练好的 BP 神经网络，这样 BP 神经网络输出的就是 D-S 证据理论所要得到的基本概率赋值，然后利用该基本概率赋值对目标进行 D-S 融合识别，经仿真实验表明该融合算法，识别率达到 90%以上，目标识别的精度明显升高。在实验室现有条件下，本次试验通过布置在水槽中的一些传感器节点来模拟水下传感器网络。首先节点将采集的水下目标的特征数据发送给网关，网关再通过串口将数据传送到网络控制系统的数据库中；然后在数据库中通过调用 matlab 程序完成对目标的分类识别；最后利用嵌入式 web 实现对目标识别结果的远程监测。关键字： 目标识别；特征提取；神经网络分类器；遗传算法；D-S 融合；江苏科技大学工学硕士学位论文IIAbstractIIIAbstractWith the development of science and technology,with the needs for national security,it has become more and more important for us to identify the underwater target. Underwater target recognition is one of the three key technical （exploration，orientation，recognition） in the development of acoustic equipment. It is an important symbol of the intelligentized exploration system and is always one of the difficult problems，which are urgent to be resolved in the sonar information processing theory. Developing the research in this field has the most important practical meaning and martial value.The recognition of underwater targets include active and passive identification.The passive recognition is our work.Firstly ,we extract the feature of the radiated noise form underwater targets.secondly designed a classifier，finally，in order to identify the underwater target ，we sent the eigenvectors which can reflect the characteristics of the underwater target to the classifier .In the stage of feature extraction,in order to get the power spectrum of the signal , people FFT(Fast Fourier transform)the signal form underwater targets.In the process of feature extraction of power spectral, the main methods we used are included continuous spectrum、line spectrum、 modulated continuous spectrum、modulated line spectrum feature extraction.In this case ,we can get eigenvectors based on different methods of feature extraction.After getting the feature vectors of the targets，in order to identify the target，we designed a classifier based on genetic and BP neural network.After the simulation ，it can be seen that the classifier identify the underwater target effectively,whose recognition accuracy is 85% or more. In order to show the target identification based on underwater sensor networks ,we use a method based on D-S evidence theory to recognize the targets. The process is：firstly，the BP neural network must been trained .secondly，putting the feature vector which is described above into the trained BP neural network，in this way, the output form BP neural network is the basic probability assignment which is needed for D-S evidence theory.Finally,we recognized the target with the method of D-S theory. After the simulation, it can be seen that this method can improve the recognition accuracy Significantly, whose recognition accuracy is 90% or more.Under the existing conditions of the laboratory,we simulated the underwater sensor 江苏科技大学工学硕士学位论文IVnetworks by arranging in a number of sensor nodes. Firstly ,the ensor nodes sent the collected dates to the gateway，the gateway transfer the data to the datebase of the network control system with the serial port. Secondly ,the target is identified in the database by calling matlab program,Finally，achieving the remote monitoring of recognition results with the embedded web.Key words: target recognition ；feature extraction；neural network classifier；genetic algorithm；D-S fusion；目 录V目 录摘 要 IAbstract .III第 1 章 绪论. 11.1 课题研究背景及意义 .11.1.1 课题研究背景 .11.1.2 课题研究意义 .21.2 水下传感器网络概述 .21.2.1 水下传感器网络概念 .21.2.2 水下传感器网络系统结构 .31.3 国内外研究现状与发展趋势 .31.3.1 国内外研究现状 .31.3.2 发展趋势 .41.4 本文的主要工作 .5第 2 章 水下目标辐射噪声信号的特征提取. 72.1 引言 .72.2 水下目标辐射噪声 .72.3 目标特征识别 .82.3.1 信号的功率谱 .92.3.2 信号的线谱 .112.3.3 信号的连续谱 .132.3.4 信号的调制线谱和调制连续谱 .142.4 本章小结 .17第 3 章 神经网络分类器的研究与设计 183.1 引言 .183.2 BP 神经网络算法研究 .183.2.1 BP 神经网络概述 .183.2.2 BP 算法的优缺点及改进 .183.3 遗传算法 .203.3.1 遗传算法概要 .213.3.2 遗传算法特点 .223.3.3 标准遗传算法 .233.3.4 遗传算法的不足与改进 .233.4 基于自适应遗传 BP 算法神经网络分类器设计 .243.4.1 自适应遗传算法 .243.4.2 自适应遗传 BP 算法 .253.4.3 神经网络分类器设计 .263.5 实验结果分析 .293.6 本章小结 .30第 4 章 D-S 证据理论在水下目标融合识别中的研究. 31江苏科技大学工学硕士学位论文VI4.1 引言 .314.2 数据融合基本概述 .314.2.1 数据融合的含义 .314.2.2 数据融合的通用模型 .314.2.3 数据融合的分类 .324.2.4 信息融合系统的系统结构 .354.2.5 数据融合的主要技术方法 .384.2.6 数据融合的应用领域 .394.3 D-S 证据理论概述 .394.3.1 证据理论的基本概率 .394.3.2 证据理论的组合规则 .414.3.3 基于证据理论的决策 .424.4 证据组合方法的优缺点及其改进 .424.4.1 证据理论的主要优缺点 .424.4.2 证据组合方法的改进 .434.5 基本概率赋值的获取（D-S 与 BP 神经网络结合） .444.5.1 BP 神经网络参数的设定 .444.5.2 D-S 证据理论与 BP 神经网络结合研究 .444.6 D-S 证据理论在目标识别中的应用 .454.7 D-S 证据理论目标识别结果分析 .474.8 本章小结 .47第 5 章 基于水下传感器网络的目标识别系统实验研究 485.1 引言 .485.2 水下传感器网络技术研究 .485.2.1 水下传感器网络通信技术分析 .485.2.2 跨层设计思想 .505.2.3 水下传感器网络能量效率分析 .515.3 基于水下传感器网络的目标识别系统总体概要 .525.3.1 基于水下传感器网络的目标识别系统工作原理 .525.3.2 水声节点通信实验研究 .535.4 远程监控系统介绍 .555.4.1 系统硬件介绍 .555.4.2 系统软件部分介绍 .575.4.3 嵌入式 web 简介 .615.5 本章小结 .63结论 65参考文献 .67攻读硕士期间申请的专利 71致 谢. 73ContentsVIIContentsAbstract（Chinese） .IAbstract（English） .IIIChapter 1 Introduction.11.1 Research background and significance .11.1.1 Subject background.11.1.2 Research significant .21.2 Overview of underwater sensor network.21.2.1 The concept of underwater sensor network .21.2.2 The struct of underwater sensor network .31.3 Domestic and international development trend .31.3.1 Domestic application status.31.3.2 Development trend .41.4 Primary work.5Chapter 2 The feature extraction of radiated noise from underwater target.72.1 Introduction.72.2 Radiated noise from underwater target .72.3 Target feature recognition .82.3.1 Power spectrum of the signal .92.3.2 Line spectrum of the signal .112.3.3 Continuous spectrum of the signal .132.3.4 Modulated line and continuous spectrum of the signal .142.4 Summary .17Chapter 3 The researching and design of neural network classifier. 183.1 Introduction.183.2 The researching of BP neural network.183.2.1 The outline of BP neural network .183.2.2 The learning steps of BP neural network .183.3 Genetic algorithms .203.3.1 Summary of the genetic algorithms .213.3.2 The feature of genetic algorithms.223.3.3 Standard genetic algorithms .233.3.4 Difference and improvement of genetic algorithms .233.4 The designed of GABP and neural network classifier .243.4.1 Adaptive genetic algorithm .243.4.2 The design of adaptive genetic BP algorithm.253.4.3 The design of BP neural network classifier.263.5 The analysis of experimental results .293.6 Summary .30Chapter 4 D-S evidence used in the recognition of underwater targets .314.1 Introduction.314.2 Based overview of data fusion .314.2.1 The meaning of data fusion .31江苏科技大学工学硕士学位论文VIII4.2.2 The general model of data fusion .314.2.3 The classification of data fusion.324.2.4 The struct of data fusion system.354.2.5 The main method of data fusion.384.2.6 The application of data fusion .394.3 Overview of D-S evidence theory .394.3.1 The basic concept of D-S evidence theory .394.3.2 Combinated rule of D-S evidence theory .414.3.3 Decision based on D-S evidence theory .424.4 Advantage、disadvantage and improvement of evidence combination .424.4.1 Advantage and disadvantage of evidence combination.424.4.2 The improvement of evidence combination .434.5 Access to basic probability assignment.444.5.1The parameter settlement of BP neural network .444.5.2 D-S evidence theory and BP neural network research .444.6 D-S ev