【摘要】：基于信號細(xì)微特征分析的輻射源個體識別研究起源于非合作通信領(lǐng)域。所謂細(xì)微特征,指的是信號個體或設(shè)備個體由于發(fā)射機(jī)設(shè)備或者傳輸信道的影響,使得接收機(jī)接收到的信號所帶有的能夠作為個體身份標(biāo)識的差異。區(qū)別于傳統(tǒng)輻射源識別理論的是,傳統(tǒng)的輻射源信號偵查識別的目的在于獲取所傳輸?shù)耐ㄐ判畔?而輻射源個體識別的目的是通過一定的信號處理過程,提取出隱藏在通信信息中的細(xì)微差異,從而識別、判斷出對方輻射源的相關(guān)情報。如何選擇有效的信號處理方法,實(shí)時、準(zhǔn)確地分析、提取出這些細(xì)微差異特征是近些年來的研究熱點(diǎn)。針對這一問題,本文深入研究了基于細(xì)微特征分析的輻射源信號以及設(shè)備個體識別的方法。論文的研究內(nèi)容主要包括以下幾個方面:建立了輻射源個體識別的系統(tǒng)模型,分析了細(xì)微特征產(chǎn)生的機(jī)理,研究了典型的輻射源細(xì)微特征提取方法,為后續(xù)細(xì)微特征分析新方法的研究提供了良好的理論基礎(chǔ)。基于熵特征提取的輻射源識別方法。由于不同的信息熵能從不同的角度描述信號的差異性,本文提出了基于多維信息熵模型的識別模型,在此基礎(chǔ)上研究了多維特征加權(quán)的方法。在信號識別環(huán)節(jié),比較了基于歐氏距離、人工智能分類器以及所提出的特征加權(quán)方法的識別性能。研究了基于參數(shù)估計的輻射源識別方法。從細(xì)微特征分析的思路出發(fā),提取出體現(xiàn)不同參數(shù)信號的個體特征,驗(yàn)證了所提取的特征在不穩(wěn)定信噪比環(huán)境下的識別性能。然后利用特征非線性擬合的方法,來識別不同參數(shù)的線性調(diào)頻信號個體。研究了基于振蕩器非線性特征的輻射源識別方法。不同的通信設(shè)備由于自身器件非線性特征的差異,會使發(fā)送的信號含有設(shè)備的個體差異信息。提出了基于局部散布差異特征提取的設(shè)備非線性分析方法,并研究了其識別性能。
[Abstract]:Individual recognition of emitter based on signal fine feature analysis originates from the field of non-cooperative communication. The so-called fine characteristic refers to the signal individual or the device individual because of the transmitter equipment or the transmission channel influence, causes the receiver to receive the signal with the ability to act as the individual identification difference. Different from the traditional emitter recognition theory, the purpose of traditional emitter signal detection and recognition is to obtain the transmitted communication information, and the purpose of emitter individual identification is to pass a certain signal processing process. The subtle differences hidden in the communication information are extracted to identify and judge the relative information of the other side's emitter. How to select effective signal processing methods to analyze and extract these subtle features in real time and accurately is a hot topic in recent years. Aiming at this problem, the emitter signal based on fine feature analysis and the method of device individual identification are studied in this paper. The main contents of this paper are as follows: the system model of individual identification of emitter is established, the mechanism of fine feature generation is analyzed, and the typical methods of extracting subtle feature of radiation source are studied. It provides a good theoretical basis for the further study of the new method of fine feature analysis. Emitter recognition method based on Entropy feature extraction. Because different information entropy can describe the difference of signal from different angles, a recognition model based on multidimensional information entropy model is proposed in this paper. In signal recognition, the recognition performance based on Euclidean distance, artificial intelligence classifier and the proposed feature weighting method is compared. The emitter recognition method based on parameter estimation is studied. Based on the idea of fine feature analysis, the individual features of different parameter signals are extracted, and the recognition performance of the extracted features in unstable SNR environment is verified. Then the feature nonlinear fitting method is used to identify the individual of LFM signal with different parameters. The recognition method of emitter based on nonlinear characteristics of oscillator is studied. Because of the difference of the nonlinear characteristics of the devices, different communication devices will make the transmitted signals contain the individual difference information of the devices. A nonlinear analysis method based on local dispersion difference feature extraction is proposed and its recognition performance is studied.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN97

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