本文選題：通信信號　切入點：單通道盲源分離　出處：《蘭州理工大學(xué)》2014年碩士論文

【摘要】：隨著現(xiàn)代通信技術(shù)的發(fā)展和國防科技信息化進(jìn)程的加快,全球通信業(yè)務(wù)需求迅速增長,無線通信基站數(shù)量急劇增多,使得電磁通信環(huán)境復(fù)雜化,頻譜資源利用緊張化,干擾噪聲種類多樣化；進(jìn)而導(dǎo)致單通道時頻混疊信號在軍用電子偵察、無線電頻譜監(jiān)測、緊急救援等通信應(yīng)用環(huán)境中普遍存在。對于單通道盲源分離的方法,利用陣列信號處理的傳統(tǒng)盲源分離算法以及時頻域、空域和碼域濾波的方法都不再適用。因此,研究如何在復(fù)雜無線通信環(huán)境中實現(xiàn)單通道盲源分離具有重要意義。 本文在分析和歸納總結(jié)現(xiàn)有盲源分離基本原理和方法的基礎(chǔ)上,探究了通信信號循環(huán)譜域的可分離機(jī)理及構(gòu)建循環(huán)譜域濾波器實現(xiàn)單通道通信信號的盲源分離方法。 研究發(fā)現(xiàn)循環(huán)平穩(wěn)性可以很好的反應(yīng)通信信號的本質(zhì)特征,從通信信號的循環(huán)譜估計方法及循環(huán)譜性質(zhì)出發(fā),提出一種基于時變ARV模型的循環(huán)譜估計算法。算法將通信信號用時變ARV模型表示,通過基時間函數(shù)展開將線性非平穩(wěn)問題轉(zhuǎn)化為線性時不變問題,利用協(xié)方差矩陣以及譜相關(guān)理論估計出信號的循環(huán)譜；同時對一些常見調(diào)制信號循環(huán)譜進(jìn)行驗證,理論分析證明循環(huán)譜反應(yīng)了調(diào)制信號載頻和碼元周期,通過循環(huán)譜估計可確定循環(huán)譜域濾波器的頻移量。 理論分析證明,通信信號在循環(huán)譜域具有獨立性和稀疏性,可以通過濾波的方法實現(xiàn)單通道通信信號的盲源分離。為此,本文在維納濾波器及盲自適應(yīng)頻移濾波器結(jié)構(gòu)和原理的基礎(chǔ)上,將已確定的頻移量應(yīng)用于線性共軛線性頻移濾波器中得到一種能同時分離出兩路源信號的單通道盲源分離方法。最后,在MATLAB環(huán)境下對混有白噪聲的QPSK和BPSK兩路時頻重疊信號進(jìn)行仿真驗證,結(jié)果表明該方法可有效分離出兩路源信號。
[Abstract]:With the development of modern communication technology and the acceleration of national defense science and technology informatization process, the demand for global communication services is growing rapidly, the number of wireless communication base stations is increasing rapidly, which makes the electromagnetic communication environment complicated and the utilization of spectrum resources tense. The variety of interference noise leads to the existence of single-channel time-frequency aliasing signals in military electronic reconnaissance, radio spectrum monitoring, emergency rescue and other communication applications. The traditional blind source separation algorithm based on array signal processing is no longer applicable in time-frequency domain, spatial domain and code domain filtering. Therefore, it is of great significance to study how to achieve single-channel blind source separation in complex wireless communication environments. On the basis of analyzing and summarizing the basic principles and methods of blind source separation, this paper probes into the detachable mechanism of cyclic spectrum domain of communication signal and the blind source separation method of single channel communication signal by constructing cyclic spectral domain filter. It is found that the cyclic stationarity can well reflect the essential characteristics of the communication signal. Based on the method of cyclic spectrum estimation and the properties of the cyclic spectrum of the communication signal, A cyclic spectrum estimation algorithm based on time-varying ARV model is proposed, in which the communication signal is represented by time-varying ARV model, and the linear non-stationary problem is transformed into a linear time-invariant problem by the basis time function expansion. The cyclic spectrum of signals is estimated by covariance matrix and spectral correlation theory, and the cyclic spectrum of some common modulated signals is verified. The theoretical analysis shows that the cyclic spectrum reflects the carrier frequency and symbol period of modulated signals. The frequency shift of cyclic spectral domain filter can be determined by cyclic spectrum estimation. The theoretical analysis shows that the communication signal is independent and sparse in the cyclic spectral domain, and can be separated from the blind source of the single channel communication signal by filtering. Based on the structure and principle of Wiener filter and blind adaptive frequency shift filter, Applying the determined frequency shift to linear conjugate linear frequency shift filter, a single channel blind source separation method can separate two source signals simultaneously. Finally, Two time-frequency overlapped signals of QPSK and BPSK mixed with white noise are simulated in MATLAB environment. The results show that the proposed method can effectively separate two source signals.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.23

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 汪軍，，何振亞;瞬時混疊信號盲分離[J];電子學(xué)報;1997年04期

2 魏安全;沈連豐;;CFE下FRESH濾波器性能分析及CFE校正[J];電子與信息學(xué)報;2008年04期

3 涂世龍;陳越新;鄭輝;;利用糾錯編碼的同頻調(diào)制混合信號單通道盲分離[J];電子與信息學(xué)報;2009年09期

4 何繼愛;裴承全;鄭玉峰;;穩(wěn)定分布下基于FAM的低階循環(huán)譜算法研究[J];電子學(xué)報;2013年07期

5 毋文峰;陳小虎;蘇勛家;;基于經(jīng)驗?zāi)Ｊ椒纸獾膯瓮ǖ罊C(jī)械信號盲分離[J];機(jī)械工程學(xué)報;2011年04期

6 何繼愛;裴承全;劉琳芝;;α穩(wěn)定分布下基于SSCA算法的低階循環(huán)譜算法[J];蘭州大學(xué)學(xué)報(自然科學(xué)版);2013年02期

7 蔡權(quán)偉;魏平;肖先賜;;單信道多信號分量分離[J];通信學(xué)報;2006年06期

8 吳量;江樺;;單通道混合信號盲分離算法[J];信息與電子工程;2012年03期

9 尤育賽,于慧敏;一種重疊紅細(xì)胞圖像的分離方法[J];中國圖象圖形學(xué)報;2005年06期

10 ;Single channel signal component separation using Bayesian estimation[J];Journal of Systems Engineering and Electronics;2007年01期

相關(guān)博士學(xué)位論文 前2條

1 陸鳳波;復(fù)雜電磁環(huán)境下的欠定盲源分離技術(shù)研究[D];國防科學(xué)技術(shù)大學(xué);2011年

2 明陽;基于循環(huán)平穩(wěn)和盲源分離的滾動軸承故障特征提取方法研究[D];上海交通大學(xué);2013年

本文編號：1683966