本文選題：水下運動目標識別 + 特征提取　； 參考：《昆明理工大學(xué)》2014年碩士論文

【摘要】：水下運動目標識別技術(shù)一直是水聲信號處理技術(shù)中的重要研究內(nèi)容。水下運動目標識別技術(shù)的研究不僅對國防建設(shè)有著非常重要的價值,對于商用和民用領(lǐng)域也有著重要的應(yīng)用價值,因此受到世界各國的廣泛關(guān)注,是當今世界各國相關(guān)領(lǐng)域的研究熱點。本文主要針對被動聲納信號識別技術(shù)、圍繞信號特征提取方法和分類器的設(shè)計進行研究,并通過實驗仿真,對本文提出的分類方法進行了驗證。 特征提取和選擇是目標識別過程中的首要環(huán)節(jié),提取有效而穩(wěn)定可靠的特征是保證分類識別準確率的前提。本文首先深入探討了小波變換和小波包變換的基本概念和原理,引入小波包分析技術(shù)提取了水下運動目標被動聲納信號的能量特征；然后,探索了經(jīng)驗?zāi)Ｊ椒纸馑惴ǖ脑?將絕對均值引入到水下運動目標被動聲納信號的特征提取中來,提出了在經(jīng)驗?zāi)Ｊ椒纸獾幕A(chǔ)上對分解后得到的固有模態(tài)函數(shù)的絕對均值進行計算的特征提取方法,并且從數(shù)據(jù)融合的角度出發(fā),結(jié)合特征融合技術(shù),構(gòu)造了一種新的分類特征向量。 分類器的設(shè)計是目標識別過程中的第二環(huán)節(jié),分類器設(shè)計的好壞和適應(yīng)性將直接影響整個系統(tǒng)最終的識別性能。本文詳細討論了模糊C均值(FCM)算法的原理及神經(jīng)網(wǎng)絡(luò)的相關(guān)理論,并就FCM算法和廣義回歸神經(jīng)網(wǎng)絡(luò)(GRNN)的優(yōu)缺點進行了分析：基于FCM算法的性能在很大程度上依賴于隨機的初始聚類中心,本身是無監(jiān)督算法,容易陷入局部極值；而廣義回歸神經(jīng)網(wǎng)絡(luò)(GRNN)隱含層結(jié)點中的作用函數(shù)采用高斯函數(shù),具有全局逼近能力�；诖�,提出了基于FCM和GRNN的一種結(jié)合算法,形成一種新分類器,對前述的特征向量進行分類識別。實驗結(jié)果表明,基于FCM和GRNN的結(jié)合算法的目標正確識別率相對較高,達到了研究目的。
[Abstract]:Underwater moving target recognition technology has been an important research content in underwater acoustic signal processing technology. The research of underwater moving target recognition technology not only has very important value for national defense construction, but also has important application value for commercial and civil fields. Nowadays, it is a hot spot in the relevant fields of the world. This paper mainly focuses on the passive sonar signal recognition technology, focusing on the signal feature extraction method and the design of the classifier, and through experimental simulation, the proposed classification method is verified. Feature extraction and selection is the first step in the process of target recognition, and the extraction of effective and stable features is the prerequisite to ensure the accuracy of classification recognition. In this paper, the basic concepts and principles of wavelet transform and wavelet packet transform are discussed, the energy characteristics of passive sonar signal of underwater moving target are extracted by wavelet packet analysis technique, and the principle of empirical mode decomposition algorithm is explored. The absolute mean value is introduced into the feature extraction of passive sonar signal of underwater moving target. Based on the empirical mode decomposition, a feature extraction method for calculating the absolute mean value of the decomposed inherent mode function is proposed. From the point of view of data fusion, a new classification feature vector is constructed based on feature fusion technology. The design of classifier is the second step in the process of target recognition. The design and adaptability of classifier will directly affect the final recognition performance of the whole system. In this paper, the principle of fuzzy C-means (FCM) algorithm and the related theory of neural network are discussed in detail. The advantages and disadvantages of FCM algorithm and generalized regression neural network (GRNN) are analyzed. The performance of FCM algorithm depends on the random initial clustering center to a great extent, and it is an unsupervised algorithm, which is easy to fall into local extremum. The generalized regression neural network (GRNN) uses Gao Si function for the function in the hidden layer node, so it has the ability of global approximation. Based on this, a combined algorithm based on FCM and GRNN is proposed to form a new classifier to recognize the feature vectors mentioned above. The experimental results show that the combined algorithm based on FCM and GRNN has a relatively high recognition rate and achieves the purpose of the research.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN912.3

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