在非協作通信中，需要對接收的信號進行調制方式的自動識別。在高階累積量域內構造信號識別的特征向量，采用基于二叉樹的支持向量機將識別特征向量映射到高維空間并構造最優分類超平面，實現對數字調制信號的自動識別。該算法不僅結構簡單、計算量小，而且解決了樣本在低維空間中的不可分問題，具有良好的泛化推廣能力。理論分析和仿真結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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