本文選題：調(diào)制識別 + 循環(huán)平穩(wěn)理論　； 參考：《蘭州理工大學》2014年碩士論文

【摘要】：復雜電磁環(huán)境下的數(shù)字調(diào)制信號自動識別是根據(jù)少有的先驗知識將一個未知調(diào)制方式的通信信號歸入相應的調(diào)制種類,為后續(xù)的進一步信號分析與處理提供可靠依據(jù)。在頻譜監(jiān)測、緊急救援與電子對抗等場合中,調(diào)制識別過程有著重要的作用。近幾十年,學者們在此方向做了很多相關的研究工作,并且提出了一些可靠的通信信號調(diào)制識別算法。但是研究發(fā)現(xiàn),很多方法在復雜通信電磁環(huán)境下工作的效率不是很好。因此,本文基于調(diào)制識別方面的國內(nèi)外最新研究成果,在復雜電磁環(huán)境下,結合循環(huán)平穩(wěn)理論、數(shù)字信號處理理論,研究如何利用能夠有效表征信號差異的特征完成對數(shù)字調(diào)制信號的識別。 在此研究背景下,本文主要工作包括以下幾點： (1)對經(jīng)典的基于統(tǒng)計特征的調(diào)制識別算法進行了研究與分析,根據(jù)特征所提取信息的變換特性,將其分為兩大類：基于瞬變信息與緩變信息的統(tǒng)計特征,并對他們進行了綜述。 (2)通過對調(diào)制信號經(jīng)過非線性系統(tǒng)后功率譜的研究,發(fā)現(xiàn)不同的數(shù)字通信調(diào)制信號具有不同的離散譜線,本文利用AR模型提取信號的譜線特征,實現(xiàn)對常見數(shù)字調(diào)制信號的識別,并在MATLAB環(huán)境下仿真驗證。 (3)根據(jù)通信信號循環(huán)平穩(wěn)特性與LPTV模型的對應關系,將數(shù)字調(diào)制信號構建為相應的LPTV模型,估計模型的結構參數(shù),在此基礎上提取相應的循環(huán)譜特征,完成對數(shù)字調(diào)制信號的識別。
[Abstract]:Automatic recognition of digital modulation signals in complex electromagnetic environment is to classify a communication signal of unknown modulation mode into corresponding modulation types according to rare prior knowledge, which provides a reliable basis for further signal analysis and processing. Modulation recognition plays an important role in spectrum monitoring, emergency rescue and electronic countermeasures. In recent decades, scholars have done a lot of research in this direction, and proposed some reliable communication signal modulation recognition algorithm. However, it is found that many methods are not efficient in complex communication electromagnetic environment. Therefore, based on the latest research results of modulation recognition at home and abroad, in the complex electromagnetic environment, combined with the cyclic stationary theory, digital signal processing theory, This paper studies how to use the features which can effectively represent the difference of signal to realize the recognition of digital modulation signal. In this context, the main work of this paper includes the following: 1) the classical modulation recognition algorithms based on statistical features are studied and analyzed. According to the transformation characteristics of the information extracted from the features, they are divided into two categories: the statistical features based on transient information and slow change information, and they are summarized. 2) by studying the power spectrum of modulation signal after passing through nonlinear system, it is found that different digital communication modulation signal has different discrete spectral lines. In this paper, AR model is used to extract the spectral line characteristics of the signal. The recognition of common digital modulation signals is realized and simulated in MATLAB environment. 3) according to the corresponding relationship between the cyclic stationary characteristic of the communication signal and the LPTV model, the digital modulation signal is constructed into the corresponding LPTV model, the structural parameters of the model are estimated, and the corresponding cyclic spectrum features are extracted. The recognition of digital modulation signal is completed.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN911.3

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本文編號：1986111