特征提取与选择是模式识别系统的关键环节,关系到系统的最终识别效果。针对通信信号调制识别中特征提取这一重要步骤,提出了熵值分析方法。对信号作FFT和Chirp-Z变换后,求取信号的香农熵值和指数熵值特征。仿真结果表明,FFT和Chirp-Z变换后的信号分别在信噪比大于20dB、10dB时,熵值曲线趋于平稳。用熵值分析法提取出的信号特征受噪声影响较小,并且得到的熵值曲线比较平稳,有利于区分信号的调制方式,具有很好的应用价值。

Feature extraction and selection are critical for pattern recognition system; they are related with the final identification effect of the system. In the paper, a method of entropy analysis was proposed for extracting the features of signals, which was an important step in the modulation recognition of communication signals. After transforming the signals through FFT and Chirp-Z transform, the Shannon entropy and the index entropy were found as the identification features. The simulation results show that after FFT and Chirp-Z transform, when the SNR is greater than 20dB, 10dB, the entropy value curves are stabilized respectively. Using the entropy analysis method to extract the signal characteristics is less affected by noise, and the entropy curves are relatively stable. It’s more conducive to distinguish the signal modulation and has a very good application value.