本文關(guān)鍵詞： 輻射源個(gè)體識別 局部積分雙譜 矩形積分雙譜 最大比重區(qū)間 自適應(yīng)組合核函數(shù)　出處：《西安電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：通信輻射源個(gè)體識別,又稱輻射源指紋識別,是近來通信對抗領(lǐng)域一個(gè)重要的研究課題。它是指對接收的通信信號進(jìn)行特征提取,并根據(jù)已有的先驗(yàn)信息確定產(chǎn)生信號的輻射源個(gè)體的過程。因此對輻射源信號細(xì)微特征進(jìn)行分析并提取出不同于其他輻射源信號的特征對于輻射源個(gè)體識別過程極其重要。本文討論了不同局部積分雙譜特征提取方法,通過實(shí)驗(yàn)研究比較了現(xiàn)有的幾種局部積分雙譜以及選擇雙譜的優(yōu)缺點(diǎn)。重點(diǎn)研究了矩形積分雙譜方法提取信號特征,由于這種積分雙譜不僅具有時(shí)移不變性、尺度變換性、相位保持性,而且能夠較好的無遺漏和無重復(fù)的對雙譜值進(jìn)行采樣。在矩形積分雙譜的基礎(chǔ)上采用最大比重區(qū)間的改進(jìn)方法,剔除了貢獻(xiàn)小甚至負(fù)作用的雙譜值,減少了冗余量及這部分帶來的噪聲值。實(shí)驗(yàn)結(jié)果表明,改進(jìn)方法較改進(jìn)前識別率有所提高,同時(shí)在不同信噪比下改進(jìn)方法也具有較好的識別率。我們采用自適應(yīng)組合核函數(shù)主成分分析方法,由于該核函數(shù)能夠較好的兼顧全局特征和局部特征,大幅度的降低了特征矢量維數(shù),實(shí)驗(yàn)結(jié)果驗(yàn)證該方法用于特征矢量維數(shù)的約簡,在保證識別率的情況下,運(yùn)算效率大幅度挺高。
[Abstract]:Individual identification of communication emitter, also called fingerprint recognition of emitter, is an important research topic in communication countermeasure field recently. It refers to the feature extraction of the received communication signal. Based on the prior information available, the process of identifying the individual source of the emitter signal is determined. Therefore, the fine features of the emitter signal are analyzed and the characteristics different from those of the other emitter signals are extracted for the individual identification process of the emitter source. In this paper, different local integral bispectral feature extraction methods are discussed. The advantages and disadvantages of several kinds of local integral bispectrum and selective bispectrum are compared by experiments. The rectangular integral bispectrum method is used to extract the signal features, because this integral bispectrum is not only time-invariant, but also scale-transforming. On the basis of rectangular integral bispectrum, the improved method of maximum specific gravity interval is adopted to eliminate the bispectral value with little contribution or even negative effect. The redundancy and the noise caused by this part are reduced. The experimental results show that the improved method has higher recognition rate than that before the improvement. At the same time, the improved method also has a better recognition rate under different SNR. We adopt the adaptive combined kernel function principal component analysis method, because the kernel function can better take into account the global and local features. The experimental results show that this method is used to reduce the dimension of feature vector, and the efficiency of the algorithm is very high under the condition of guaranteed recognition rate.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN975

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 ;INDIVIDUAL COMMUNICATION TRANSMITTER IDENTIFICATION BASED ON MULTIFRACTAL ANALYSIS[J];Journal of Electronics;2005年04期

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本文編號：1521526