基于时频域特征和分层决策的通信信号调制识别

针对实际通信信号具有时变、非平稳的特性,将时频分布引入通信信号调制类型识别,提出了新的品质优良的时频域识别特征和使用时频域特征的分层决策分类器。即以基于Wigner Ville分布(WVD)的一阶时间矩特征和基于Margenau Hill分布(MHD)的局部时频域特征作为分类特征向量,通过特征向量与判决阈值的逐层比较以实现通信信号调制分类。仿真结果表明,该方法在低信噪比下的识别性能明显优于基于单纯的时域特征或频域特征的传统方法。

Modulation recognition of communication signals based on time-frequency domain features and hierarchical decision classifier

New time-frequency domain features and hierarchical decision classifier are proposed for the modulation recognition of communication signals which are generally nonstationary and time-varying. Time-frequency moment features based on Wigner-Ville distribution and local time-frequency distribution features based on Margenau-Hill distribution are used as the classification vectors. By comparing these vectors with their suitable thresholds, the classification of different digital modulation types is achieved. Computer simulation results show that the proposed method has much better performance than the classical methods based on mere time domain features or frequency domain features under the condition of low signal-to-noise ratio.