【摘要】：調(diào)制類型是通信信號(hào)的一個(gè)重要的特征,隨著無(wú)線通信技術(shù)的發(fā)展,調(diào)制類型日益復(fù)雜。傳統(tǒng)基于人工的調(diào)制識(shí)別技術(shù)已經(jīng)不能滿足實(shí)際的需求,使用DSP、FPGA和軟件無(wú)線電技術(shù)的自動(dòng)調(diào)制識(shí)別成為一種趨勢(shì)。自動(dòng)調(diào)制識(shí)別技術(shù)被廣泛運(yùn)用于艦船電磁干擾監(jiān)測(cè)、無(wú)線電監(jiān)聽(tīng)等領(lǐng)域。本文研究了通信信號(hào)調(diào)制參數(shù)估計(jì)和調(diào)制識(shí)別的基本原理。針對(duì)2ASK、4ASK、2FSK、4FSK、2PSK、AM、FM、LSB和USB共九種調(diào)制信號(hào),提出了一種基于瞬時(shí)統(tǒng)計(jì)特征和決策樹(shù)分類器的調(diào)制識(shí)別算法。相比以往的算法,本文的算法優(yōu)勢(shì)在于能夠滿足實(shí)際硬件實(shí)現(xiàn)時(shí)所需的速度快、所需采樣點(diǎn)少、適合隨機(jī)碼元條件。識(shí)別算法使用一種新的中心化參數(shù),改善了{(lán)2ASK,4ASK}和{2FSK,4FSK}識(shí)別效果。使用一種門(mén)限自動(dòng)調(diào)節(jié)算法,通過(guò)選取信噪比參考參數(shù),實(shí)現(xiàn)信噪比的粗估計(jì),使得判決門(mén)限可以自動(dòng)調(diào)整,提高了識(shí)別算法對(duì)信噪比的適應(yīng)性。相比于以往很多算法,本算法在載波頻率未知的情況下依然有效,利用高精度的載頻估計(jì)算法-CZT變換法獲取信號(hào)的載波頻率,以滿足提取非線性相位的精度要求。通過(guò)MATLAB仿真,在采樣點(diǎn)為1024(16個(gè)隨機(jī)碼元)、未知載頻條件下,驗(yàn)證了本算法具有較好的效果。在信噪比不小于10dB時(shí),平均識(shí)別正確率達(dá)到93.27%,在SNR不小于15dB時(shí),平均識(shí)別率超過(guò)99.0%。設(shè)計(jì)完成了基于DSP+FPGA結(jié)構(gòu)的調(diào)制識(shí)別的硬件平臺(tái),完成了硬件電路的原理圖和PCB設(shè)計(jì)工作。對(duì)DSP板進(jìn)行了信號(hào)完整性分析(SI),使用SigXploer完成了部分電路的SI仿真,以保證硬件電路運(yùn)行的穩(wěn)定性和可靠性,并完成了硬件平臺(tái)的調(diào)試工作。在CCS平臺(tái)下對(duì)識(shí)別算法的C語(yǔ)言程序進(jìn)行了軟件仿真,統(tǒng)計(jì)了識(shí)別算法的識(shí)別率。最后使用所設(shè)計(jì)硬件平臺(tái)對(duì)信號(hào)發(fā)生器產(chǎn)生的5種調(diào)制信號(hào)進(jìn)行了實(shí)時(shí)識(shí)別,達(dá)到了預(yù)期效果。
[Abstract]:Modulation type is an important characteristic of communication signal. With the development of wireless communication technology, modulation type is becoming more and more complex. The traditional manual modulation recognition technology can not meet the actual needs. Automatic modulation recognition using DSP,FPGA and software radio technology has become a trend. Automatic modulation recognition technology is widely used in ship electromagnetic interference monitoring, radio monitoring and other fields. The basic principle of modulation parameter estimation and modulation recognition of communication signal is studied in this paper. This paper presents a modulation recognition algorithm based on instantaneous statistical features and decision tree classifier for nine kinds of modulation signals, 2ASK / 4ASK / 2FSKC / 4FSKK / 2PSK / AMK / FM / USB, which are based on instantaneous statistical features and decision tree classifier. Compared with the previous algorithms, the advantage of this algorithm is that it can meet the needs of fast hardware implementation, less sampling points, and suitable for random symbol conditions. The recognition algorithm uses a new centralization parameter to improve the recognition effect of {2ASK _ 4ASK} and {2FSKN _ 4FSK}. A threshold automatic adjustment algorithm is used to estimate the SNR by selecting the reference parameters of SNR. The decision threshold can be adjusted automatically and the adaptability of the recognition algorithm to SNR is improved. Compared with many previous algorithms, this algorithm is still effective in the case of unknown carrier frequency. The high accuracy carrier frequency estimation algorithm CZT transform is used to obtain the carrier frequency of the signal to satisfy the precision requirement of extracting nonlinear phase. The MATLAB simulation shows that the proposed algorithm is effective under the condition that the sampling point is 1024 (16 random symbols) and the carrier frequency is unknown. The average recognition accuracy is 93.27 when the SNR is not less than 10dB, and 99.0 when SNR is not less than 15dB. The hardware platform of modulation recognition based on DSP FPGA structure is designed. The schematic diagram of hardware circuit and the design of PCB are completed. Signal Integrity Analysis of DSP Board (SI), completes the SI simulation of part of the circuit using SigXploer to ensure the stability and reliability of the hardware circuit, and completes the debugging of the hardware platform. The C language program of the recognition algorithm is simulated on the CCS platform, and the recognition rate of the recognition algorithm is calculated. Finally, the designed hardware platform is used to identify the five modulation signals produced by the signal generator in real time, and the desired results are achieved.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 靳曉艷;周希元;;一種最大似然調(diào)制識(shí)別的快速算法[J];系統(tǒng)工程與電子技術(shù);2013年03期

2 賈志軍;孫洋;毛欣;;MPSK信號(hào)調(diào)制識(shí)別方法[J];四川兵工學(xué)報(bào);2013年01期

3 徐斌;雷菁;李保國(guó);;一種數(shù)字信號(hào)調(diào)制方式識(shí)別方法[J];通信技術(shù);2011年11期

4 孟玲玲;李靜;;基于循環(huán)譜相關(guān)方法的MFSK信號(hào)識(shí)別[J];無(wú)線電通信技術(shù);2010年01期

5 朱雷;程漢文;吳樂(lè)南;;利用循環(huán)譜和參數(shù)統(tǒng)計(jì)的數(shù)字調(diào)制信號(hào)識(shí)別[J];應(yīng)用科學(xué)學(xué)報(bào);2009年02期

6 王芳;張瑞華;王蘭勛;;基于小波變換的通信信號(hào)識(shí)別算法[J];通信技術(shù);2008年09期

7 宋嬌;葛臨東;;一種基于高階累積量的FSK信號(hào)識(shí)別新方法[J];通信技術(shù);2008年04期

8 包錫銳;吳瑛;周欣;;基于高階累積量的數(shù)字調(diào)制信號(hào)識(shí)別算法[J];信息工程大學(xué)學(xué)報(bào);2007年04期

9 陳健;闊永紅;李建東;馬玉寶;;基于小波變換的數(shù)字調(diào)制信號(hào)識(shí)別方法的研究[J];電子與信息學(xué)報(bào);2006年11期

10 李春輝;;調(diào)制體制識(shí)別算法綜述[J];數(shù)字通信世界;2005年11期

相關(guān)博士學(xué)位論文 前2條

1 陸明泉;多信號(hào)的調(diào)制識(shí)別技術(shù)研究[D];電子科技大學(xué);2008年

2 詹亞鋒;通信信號(hào)自動(dòng)制式識(shí)別及參數(shù)估計(jì)[D];清華大學(xué);2004年

相關(guān)碩士學(xué)位論文 前10條

1 袁本義;通信信號(hào)調(diào)制識(shí)別算法研究與實(shí)現(xiàn)[D];解放軍信息工程大學(xué);2011年

2 盧璐;通信信號(hào)調(diào)制分類識(shí)別與參數(shù)提取技術(shù)研究[D];西安電子科技大學(xué);2010年

3 郭洪志;通信信號(hào)識(shí)別系統(tǒng)的關(guān)鍵算法實(shí)現(xiàn)[D];浙江大學(xué);2010年

4 宋勇;衛(wèi)星通信信號(hào)的調(diào)制識(shí)別與參數(shù)估計(jì)[D];哈爾濱工程大學(xué);2009年

5 莫乾坤;通信信號(hào)調(diào)制識(shí)別與解調(diào)技術(shù)研究[D];上海交通大學(xué);2008年

6 魏瑾;低信噪比調(diào)制信號(hào)識(shí)別方法的研究[D];太原理工大學(xué);2008年

7 喻俊峰;通信信號(hào)調(diào)制制式識(shí)別系統(tǒng)的研究及硬件實(shí)現(xiàn)[D];哈爾濱工程大學(xué);2008年

8 盧娜;調(diào)制模式識(shí)別和信號(hào)特征提取的研究[D];西安電子科技大學(xué);2008年

9 王長(zhǎng)宇;數(shù)字信號(hào)調(diào)制方式盲識(shí)別的研究[D];哈爾濱工業(yè)大學(xué);2006年

10 張鳴;軟件無(wú)線電接收機(jī)中信號(hào)調(diào)制樣式識(shí)別算法的研究[D];西安科技大學(xué);2005年

，

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