本文選題：通信輻射源　切入點(diǎn)：調(diào)制識(shí)別　出處：《哈爾濱工程大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：當(dāng)今的時(shí)代,是無(wú)線通信技術(shù)發(fā)展極其迅猛的時(shí)代,隨著高新技術(shù)的快速發(fā)展以及戰(zhàn)爭(zhēng)形態(tài)的日益變化,信息戰(zhàn)勢(shì)必發(fā)展為未來(lái)戰(zhàn)爭(zhēng)的主要形態(tài),而通信輻射源個(gè)體識(shí)別技術(shù)是信息對(duì)抗領(lǐng)域的關(guān)鍵技術(shù)之一。通信輻射源個(gè)體識(shí)別算法主要采用的是模式識(shí)別方法。模式識(shí)別方法的一般步驟是,首先對(duì)信號(hào)進(jìn)行預(yù)處理,包括對(duì)信號(hào)進(jìn)行一定的去噪處理或進(jìn)行某種變換,然后對(duì)預(yù)處理后的信號(hào)進(jìn)行分析,提取可以代表輻射源個(gè)體特征的參量保存到數(shù)據(jù)庫(kù),作為信號(hào)的特征參量。若截獲的信號(hào)特征能與數(shù)據(jù)庫(kù)中的信號(hào)特征匹配,則認(rèn)為與該信號(hào)是同一輻射源發(fā)出,從而達(dá)到識(shí)別輻射源個(gè)體的目的。隨著通信電磁環(huán)境的日益復(fù)雜,以及通信信號(hào)的樣式的逐漸增多,如何在較低信噪比下有效的提取輻射源的個(gè)體特征,是各國(guó)學(xué)者關(guān)注的熱點(diǎn)問(wèn)題。針對(duì)輻射源個(gè)體識(shí)別中如何在低信噪比下有效的提取輻射源個(gè)體特征這一問(wèn)題,論文提出了幾種新的特征提取算法,并設(shè)計(jì)了灰色關(guān)聯(lián)分類器,對(duì)提取到的特征進(jìn)行分類識(shí)別,具體內(nèi)容如下:由于各個(gè)特征提取算法的性能都要用最終對(duì)輻射源個(gè)體的識(shí)別效果來(lái)驗(yàn)證,即需要利用分類器對(duì)提取到的信號(hào)特征進(jìn)行分類識(shí)別,因此,先對(duì)分類器的設(shè)計(jì)算法進(jìn)行了介紹,以便于在后文中對(duì)分類器的使用�；疑P(guān)聯(lián)理論主要是通過(guò)計(jì)算兩個(gè)不同離散序列的關(guān)聯(lián)度進(jìn)而判斷序列的相似關(guān)聯(lián)程度。相對(duì)于神經(jīng)網(wǎng)絡(luò)分類器而言,其實(shí)時(shí)性識(shí)別能力強(qiáng),但自適應(yīng)能力較差,針對(duì)這一問(wèn)題,首先,提出了改進(jìn)的灰色關(guān)聯(lián)算法,通過(guò)自適應(yīng)的對(duì)特征序列中各個(gè)特征的重要程度的選擇,來(lái)提高算法的自適應(yīng)能力;又針對(duì)低信噪比條件下,提取到的信號(hào)特征往往呈現(xiàn)區(qū)間分布的這一特性,提出了改進(jìn)自適應(yīng)區(qū)間的灰色關(guān)聯(lián)算法,仿真結(jié)果表明,該算法能夠?qū)崿F(xiàn)低信噪比下,對(duì)提取到的交疊信號(hào)特征進(jìn)行分類的目的。其次,針對(duì)輻射源個(gè)體識(shí)別中特征提取這一模塊,提出了基于熵云特征Holder云特征的二次特征提取算法。該算法通過(guò)計(jì)算低信噪比下不穩(wěn)定的熵特征和Holder系數(shù)特征的分布特性,即提取信號(hào)第一次特征提取到的特征分布的均值、熵、超熵這3個(gè)云模型的數(shù)字特征,進(jìn)一步對(duì)信號(hào)的離散分布特征進(jìn)行特征提取,通過(guò)二次特征提取,更為精確的刻畫(huà)了信號(hào)在低信噪比下的特征分布,再利用自適應(yīng)區(qū)間灰色關(guān)聯(lián)分類器對(duì)提取到的三維云特征進(jìn)行分類,實(shí)現(xiàn)了低信噪比下的個(gè)體識(shí)別。再次,提出了基于改進(jìn)分形盒維數(shù)的輻射源個(gè)體特征提取算法,對(duì)分形理論中的一維盒維數(shù)的基本算法進(jìn)行了改進(jìn),通過(guò)對(duì)盒維數(shù)擬合曲線的每一點(diǎn)值進(jìn)行求導(dǎo),組成待識(shí)別信號(hào)的盒維數(shù)特征向量,更精細(xì)的對(duì)信號(hào)的盒維數(shù)特征進(jìn)行了刻畫(huà),相對(duì)于傳統(tǒng)的一維盒維數(shù)特征,具有更好的識(shí)別效果。最后,提出了基于多重分形維數(shù)的輻射源個(gè)體細(xì)微特征提取算法,對(duì)不同噪聲環(huán)境下的通信電臺(tái)個(gè)體信號(hào)或是攜帶不同電臺(tái)內(nèi)部細(xì)小噪聲的電臺(tái)信號(hào)進(jìn)行多重分形維數(shù)的特征提取,通過(guò)提取離散信號(hào)不同重構(gòu)空間下的微小特征,進(jìn)而實(shí)現(xiàn)對(duì)細(xì)微特征進(jìn)行識(shí)別的目的。
[Abstract]:In today's era, is the rapid development of wireless communication technology is the era, with the rapid development of high technology and changing the form of war, information warfare will develop as a main form of future wars, and communication emitter identification technology is one of the key technology in the field of information warfare. Communication emitter identification algorithm is mainly used is the pattern recognition method. The general steps of pattern recognition methods, the signal pretreatment, including signal denoising or some transformation, and then the signal preprocessing after analysis, extraction parameters can represent the individual characteristics of the radiation source is saved to the database, as if the characteristic parameters of signal. The signal characteristics can match the intercepted signal feature and the database, and that the signal is the same radiation emitted by a source, so as to achieve the recognition of individual radiation source The purpose of communication. With the increasingly complex electromagnetic environment, and gradually increase the style of communication signals, the individual feature extraction of radiation source in low SNR effectively, is a hot topic for many scholars in different countries. In order to solve the problem of feature extraction of radiation source in low SNR effective emitter the recognition of individual papers, this paper puts forward some new feature extraction algorithm, and designed a grey correlation classifier, the extracted features to the classification, the specific contents are as follows: the performance of various feature extraction algorithms are used for final emitter recognition results to verify that signal characteristics using the classifier to extract the classification, therefore, the design of classifier algorithm was introduced to facilitate the use of classifiers in this article. The grey relational theory is mainly through the calculation of the two is not Correlation with discrete sequence similarity degree and judge sequence. Compared to the neural network classifier, strong recognition ability in fact, but poor adaptability, in order to solve this problem, firstly, puts forward the grey correlation algorithm, the important degree of each feature in the sequence of adaptive selection to improve the adaptability of the algorithm; and according to the condition of low SNR, signal feature extraction to tend to the characteristics of interval distribution, puts forward the grey correlation algorithm of adaptive interval, the simulation results show that this algorithm can achieve low SNR, the extraction of overlapping signal features for classification purposes secondly, according to the characteristics of emitter identification in the extraction module, put forward the two feature extraction algorithm entropy cloud feature Holder cloud based on features. By calculating the low SNR Than the distribution characteristics of entropy feature and Holder coefficient characteristics under unstable, that is the first time to extract the signal feature extraction feature distribution mean, entropy, entropy of the 3 super digital characteristics of cloud model, further discrete distribution of signal feature extraction, extraction of syndrome through two more accurately portray the special. The signal in low SNR distribution, 3D cloud feature by adaptive interval grey correlation classifier for extraction to classify, to realize the low signal-to-noise ratio of individual identification. Thirdly, put forward improved emitter feature extraction algorithm based on fractal box dimension, the basic algorithm of one-dimensional fractal box dimension the theory was improved, the derivation through each point of the fitting curve of the box dimension, box dimension feature vector for recognition of the signal. The signal box dimension feature of more precise moment Painting, relative to the traditional one-dimensional box dimension feature, has better recognition effect. Finally, we propose an extraction algorithm of radiation source multi fractal dimension of individual subtle features based on individual communication transmitter signals in different noise environments or feature extraction of radio signals with different radio noise of multiple internal small fractal dimension, small by extracting the discrete signal characteristics of different reconstruction space, so as to realize the purpose of identification of subtle features.

【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7

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