新特征選擇方法下的信號(hào)調(diào)制識(shí)別
Recognition of modulation signal based on a new method of feature selection
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摘要: 針對(duì)通信信號(hào)的特點(diǎn),該文提出了一種適用范圍較廣的新的特征選擇方法。它運(yùn)用高效、穩(wěn)健的遺傳算法,按照使同一類別特征的集群程度和不同類別特征的分離程度達(dá)到最佳的評(píng)價(jià)準(zhǔn)則,篩選出一高質(zhì)量的特征子集,并結(jié)合證據(jù)理論的精細(xì)結(jié)構(gòu),在一個(gè)較大的信噪比變化范圍內(nèi),得到了較高的調(diào)制識(shí)別率,計(jì)算機(jī)模擬結(jié)果證實(shí)了該方法的優(yōu)越性。
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關(guān)鍵詞:
- 特征選擇; 遺傳算法; 證據(jù)理論; 調(diào)制識(shí)別
Abstract: According to the characteristic of communication signals, a new method of feature selection is presented. In this method, the efficient and robust genetic algorithm is used to sift out qualified feature subset, under the rule of clustering level of same classifications and separating level of different classifications. With the fine structure of evidence theory, a considerable recognition ratio is obtained under a wide range of SNR environment. Finally, the effectiveness of this method is proved by computer simulation. -
Lu Mingquan, Xiao Xianci, Li Lemin, A new digital modulation recognition method using features extracted from GAR model parameters, Journal of Electronics(China), 1999, 16(3), 244-250.[2]K. Assaleh.[J].K. Farrall, R. J. Mammone, A new method of modulation classification for digitally modulated signals, Proc. MILCOM92, San Diego, CA.0712,1992:-[3]E.E. Azzouz, A. K. Nandi, Automatic identification of digital modulation types, Signal Processing, 1995, 47(4), 55-69.[4]R. Setiono, H. Liu, Neural-network feature selector, IEEE Trans. on Neural Networks, 1997,NN-8(3), 654-661.[5]呂鐵軍,魏平,肖先賜,基于分形和測度理論的信號(hào)調(diào)制識(shí)別,電波科學(xué)學(xué)報(bào),2001,16(1),123-127[6]M. Srinivas, L. M. Patnaik, Genetic algorithms: A Survey, Computer, 1994, 6(2), 17-26.[7]S.C.A. Thomopoulos, Sensor integration and data fusion, Journal of Robotic Systems, 1990,7(3), 337-372.[8]張堯庭,杜勁松,人工智能中的概率統(tǒng)計(jì)方法,北京,科學(xué)出版社,1998,31-35.[9]肖先賜,呂鐵軍,通信信號(hào)特征分析研究,研究報(bào)告,成都,電子科技大學(xué),1998. -
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