摘 要

隨著科學技術的發(fā)展，以及國防安全的需要，對于水下目標的識別已經變得越來越重要。水下目標識別是水聲裝備發(fā)展的三項關鍵技術（探測、定位、識別）之一，是探測系統(tǒng)智能化的重要標志，同時也是聲納信息理論中急待解決的難題。開展該領域的研究具有極其重要的現(xiàn)實意義與軍事價值。
水下目標識別分為主動識別和被動識別兩種，本文研究的是被動識別技術。它是將被動聲納接收的水下目標噪聲信號先進行特征提取，提取出能夠反映目標特征的特征向量，然后設計一個目標分類器，最后將提取出的能夠反映目標本質特性的特征向量送入目標分類器進行分類識別。
在特征提取階段，本文將采集的水下目標的信號進行快速傅里葉變換（FFT）得到信號的功率譜，然后對功率譜進行特征提取，其中最主要的特征提取方法包括連續(xù)譜特征提取、線譜特征提取、調制連續(xù)譜特征提取、調制線譜特征提取，這樣就可以得到信號的基于不同特征提取方法的特征向量。
在得到目標的特征向量后，首先設計一個自適應遺傳BP神經網絡分類器對目標進行分類處理，經仿真實驗表明該特征分類器能夠有效地對水下目標信號進行識別，其識別率達到了85%以上。為了體現(xiàn)基于水下傳感器網絡的目標識別，本文又采用了基于D-S證據(jù)理論的方法對目標進行融合識別，其過程為：首先訓練一個BP神經網絡，然后把上文所介紹的水下目標信號的各個特征向量輸入訓練好的BP神經網絡，這樣BP神經網絡輸出的就是D-S證據(jù)理論所要得到的基本概率賦值，然后利用該基本概率賦值對目標進行D-S融合識別，經仿真實驗表明該融合算法，識別率達到90%以上，目標識別的精度明顯升高。
在實驗室現(xiàn)有條件下，本次試驗通過布置在水槽中的一些傳感器節(jié)點來模擬水下傳感器網絡。首先節(jié)點將采集的水下目標的特征數(shù)據(jù)發(fā)送給網關，網關再通過串口將數(shù)據(jù)傳送到網絡控制系統(tǒng)的數(shù)據(jù)庫中；然后在數(shù)據(jù)庫中通過調用matlab程序完成對目標的分類識別；最后利用嵌入式web實現(xiàn)對目標識別結果的遠程監(jiān)測。

關鍵字： 目標識別；特征提??；神經網絡分類器；遺傳算法；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 錄
摘 要 …………………………………………………………………………………………………………………………… I
Abstract………………………………………………………………………………………………………………………… III
第1章 緒論……………………………………………………………………..