在非協(xié)作通信中，需要對(duì)接收的信號(hào)進(jìn)行調(diào)制方式的自動(dòng)識(shí)別。在高階累積量域內(nèi)構(gòu)造信號(hào)識(shí)別的特征向量，采用基于二叉樹的支持向量機(jī)將識(shí)別特征向量映射到高維空間并構(gòu)造最優(yōu)分類超平面，實(shí)現(xiàn)對(duì)數(shù)字調(diào)制信號(hào)的自動(dòng)識(shí)別。該算法不僅結(jié)構(gòu)簡(jiǎn)單、計(jì)算量小，而且解決了樣本在低維空間中的不可分問(wèn)題，具有良好的泛化推廣能力。理論分析和仿真結(jié)果證明了該算法的正確性和有效性。

　　Abstract:

　　In non-cooperative communication system，it is necessary to recognize the modulation type of the received signals.The algorithm utilizes characteristic vector in high order cumulant domain.SVM based on binary tree maps the characteristic vector into the high dimension space and constructs the optimal separating hyperplane in the space to classify digital modulation signals. The algorithm has simple structure and high computation efficiency， also resolves the non-separable problem in low dimension space and has high generalization performance.The efficiency of the proposed classification algorithm is verified via theoretical analysis and extensive simulations.

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