基于水下传感器网络.doc
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基于水下传感器网络,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...
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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；
Abstract
With 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 networks 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
摘 要 …………………………………………………………………………………………………………………………… I
Abstract………………………………………………………………………………………………………………………… III
第1章 绪论……………………………………………………………………..
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；
Abstract
With 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 networks 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
摘 要 …………………………………………………………………………………………………………………………… I
Abstract………………………………………………………………………………………………………………………… III
第1章 绪论……………………………………………………………………..
