聯(lián)合時域和時頻域特征的數(shù)字調(diào)制信號自動分類
發(fā)布時間：2018-09-10 15:51
【摘要】：針對ASK、PSK、QAM、FSK、MSK、LFM、OFDM信號的調(diào)制分類問題,在無任何先驗(yàn)知識的條件下,本文提出了一種基于模糊函數(shù)域、Choi-William分布時頻域和時域特征的自動分類算法。該算法首先提取信號在不同域上的特征,然后通過主成份分析法降維去除特征之間的冗余信息,再利用支持向量機(jī)分類器實(shí)現(xiàn)信號分類。為評估算法性能及優(yōu)點(diǎn),本文在不同信噪比和調(diào)制信號參數(shù)條件下,做了一系列仿真實(shí)驗(yàn),并將本文算法與其他分類識別算法進(jìn)行了對比。最后結(jié)果驗(yàn)證了本文算法的可行性和有效性,以及在低信噪比條件下較好的魯棒性。
[Abstract]:For the modulation classification of ASK,PSK,QAM,FSK,MSK,LFM,OFDM signals, an automatic classification algorithm based on the time-frequency and time-domain features of Choi-William distribution in fuzzy function domain is proposed without any prior knowledge. The algorithm firstly extracts the features of the signals in different domains, then reduces the dimension of the features by using the principal component analysis method to remove the redundant information between the features, and then realizes the signal classification by using the support vector machine classifier. In order to evaluate the performance and advantages of the algorithm, a series of simulation experiments are done under different SNR and modulation signal parameters, and the algorithm is compared with other classification and recognition algorithms. Finally, the feasibility and effectiveness of the proposed algorithm and its robustness under low signal-to-noise ratio (SNR) are verified.
【作者單位】： 武漢大學(xué)電子信息學(xué)院;
【基金】：湖北省自然科學(xué)基金青年基金項目(2015CFB202)資助課題
【分類號】：TN911.3
本文編號：2234903
[Abstract]:For the modulation classification of ASK,PSK,QAM,FSK,MSK,LFM,OFDM signals, an automatic classification algorithm based on the time-frequency and time-domain features of Choi-William distribution in fuzzy function domain is proposed without any prior knowledge. The algorithm firstly extracts the features of the signals in different domains, then reduces the dimension of the features by using the principal component analysis method to remove the redundant information between the features, and then realizes the signal classification by using the support vector machine classifier. In order to evaluate the performance and advantages of the algorithm, a series of simulation experiments are done under different SNR and modulation signal parameters, and the algorithm is compared with other classification and recognition algorithms. Finally, the feasibility and effectiveness of the proposed algorithm and its robustness under low signal-to-noise ratio (SNR) are verified.
【作者單位】： 武漢大學(xué)電子信息學(xué)院;
【基金】：湖北省自然科學(xué)基金青年基金項目(2015CFB202)資助課題
【分類號】：TN911.3
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