本文選題：被動聲納信號 + RBF神經(jīng)網(wǎng)絡　； 參考：《昆明理工大學》2014年碩士論文

【摘要】：在水聲信號處理領域,水下運動目標被動聲納信號的分析和處理一直是該領域的研究熱點,是水聲對抗及魚雷預警技術中的重要環(huán)節(jié)和關鍵技術。然而,實測的水下運動目標被動聲納信號微弱,因海水環(huán)境及聲傳播條件不確定而具有時變特性,并夾雜有大量的背景干擾噪聲難以提取有效成分。另外,實測數(shù)據(jù)需要專業(yè)設備和人員參與,其耗時長、開銷大和數(shù)據(jù)保密等因素都對被動聲納信號的分析和應用(比如用于海上部隊訓練、目標指揮系統(tǒng)的方案論證、模擬訓練以及對聲納系統(tǒng)在實驗室階段的測試等研究)形成了制約。因此,對水下運動目標被動聲納信號的模擬或仿真有著重要的現(xiàn)實價值和軍事意義。 本文詳細分析了水下運動目標輻射噪聲信號(以下簡稱為原始信號)特性,包括原始信號的數(shù)學和統(tǒng)計特性,產(chǎn)生機理等。在此基礎上對原始信號中存在的線譜和連續(xù)譜分量進行了分析和仿真。針對原始信號在復雜海洋環(huán)境傳播中受到的多種因素影響模擬了聲納端的被動聲納接收信號(以下簡稱為目標信號)。 由于神經(jīng)網(wǎng)絡具有自組織、自適應和多線程并行處理的特性,以及對于非線性系統(tǒng)良好的逼近能力,本文探索性的使用神經(jīng)網(wǎng)絡技術對原始信號經(jīng)海水非線性信道傳播的過程進行了模擬。并對RBF神經(jīng)網(wǎng)絡學習算法進行了創(chuàng)新性的研究,在深入探討K-均值聚類和傳統(tǒng)FCM算法的基礎上,提出了具有更好泛化能力和自適應性的改進型FCM算法,應用此改進算法對信號數(shù)據(jù)進行聚類分析,確定神經(jīng)網(wǎng)絡結(jié)構(gòu)和基函數(shù)參數(shù)；通過對非線性函數(shù)的逼近和預測仿真,驗證了基于本文算法設計的RBF神經(jīng)網(wǎng)絡對于非線性系統(tǒng)擬合的先進性；隨后使用本文方法對原始信號傳播至被動聲納端的非線性過程進行建模,模擬了目標信號和時域特征值,并運用相關系數(shù)和誤差指數(shù)作為評價指標對模型輸出數(shù)據(jù)和目標信號進行了相似性對比,結(jié)果證明所提出的改進算法較經(jīng)典算法優(yōu)越,達到了對水下運動目標被動聲納信號建模研究的目的。
[Abstract]:In the field of underwater acoustic signal processing , the analysis and treatment of passive sonar signal of underwater moving target has been a hot spot in this field . It is an important link and key technique in underwater acoustic countermeasure and torpedo early warning technology . However , the measured data requires professional equipment and personnel to take part in the analysis and application of passive sonar signal ( for example , it is used for the training of naval forces , the program demonstration of the target command system , the simulation training and the testing of sonar system in the laboratory stage , etc . ) . Therefore , it has important realistic value and military significance to the simulation or simulation of passive sonar signal of underwater moving target .

In this paper , the characteristics of underwater moving target radiated noise signal ( hereinafter referred to as the original signal ) are analyzed in detail , including the mathematical and statistical characteristics of the original signal , the generating mechanism , etc . On the basis of this , the line spectrum and the continuous spectral components present in the original signal are analyzed and simulated . The passive sonar receiving signal ( hereinafter referred to as the target signal ) of the sonar end is influenced by various factors which are influenced by the original signal in the propagation of complex ocean environment .

As the neural network has the characteristics of self - organizing , self - adaptive and multi - thread parallel processing , and good approximation ability for nonlinear systems , this paper simulates the process of nonlinear channel propagation of the original signal by using the neural network technique , and proposes an improved FCM algorithm with better generalization ability and adaptability based on the deep discussion of the K - means clustering and the traditional FCM algorithm .
Based on the approximation and prediction simulation of nonlinear function , the RBF neural network designed based on this algorithm is validated for nonlinear system fitting .
Then the nonlinear process of propagation of the original signal to the passive sonar end is modeled by using the method , the target signal and the time domain characteristic value are simulated , and the correlation coefficient and the error index are used as the evaluation indexes to carry out similarity comparison on the model output data and the target signal , and the result proves that the proposed improved algorithm is superior to the classical algorithm and achieves the purpose of modeling the passive sonar signal of the underwater moving target .

【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN911.7

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本文編號：1777558