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融合多特征的水声通信信号调制识别方法

Modulation classification method based on multi-feature fusion for underwater acoustic communication signals

  • 摘要: 针对水声通信信号的特征易受信道噪声和多径效应等复杂水声环境的影响导致识别率较低的问题, 提出了一种融合多特征的水声通信调制识别方法。首先, 为获取抗噪性能更强的信号特征, 设计了小波时频特征与平方功率谱、自相关谱的最强两谱线特征相融合的方法; 其次, 基于迁移学习理论构建轻量化网络模型, 以时频特征完成2FSK和4FSK信号的识别; 最后, 设计粒子群优化的支持向量机, 根据最强两谱线特征实现对非频移键控信号(包括BPSK、QPSK、DSSS和OFDM信号)的识别。仿真结果表明该方法对水声信道和环境噪声具有良好的泛化能力, 海试数据验证了该方法的识别率优于现有的神经网络模型。

     

    Abstract: Feature extraction of communication signal is easily affected by complex underwater acoustic interferences such as high noise and multipath effect, which lead to low classification rate. To solve this problem, a modulation classification method based on multi-feature fusion for underwater acoustic communication signals is proposed. First, the multi-feature fusion method is designed to obtain signal features with stronger noise immunity, including wavelet time-frequency spectrum and two stronger spectral line features from square power spectrum and autocorrelation spectrum. Secondly, based on transfer learning theory, a lightweight network model is constructed to classify 2FSK and 4FSK signals using time-frequency features. Finally, combined with support vector machine optimized by particle swarm algorithm, an interclass recognition for non-frequency shift keying signals (including BPSK, QPSK, DSSS, OFDM signals) is designed using two stronger spectral line features. The results of simulation experiments demonstrate that the proposed method has good generalization abilities and noise immunity against underwater acoustic channels. The sea trial data verifies that the proposed method outperforms common neural network models in the classification rate.

     

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