Xiuquan Li, Zhen Wang, Yeyin Jin, Jing Chen, Zhenfei Li
Hangzhou Institute of Computer External Equipment, Hangzhou, China.
School of Electronic Information, Hangzhou Dianzi University, Hangzhou, China.
Sci Rep. 2024 Aug 19;14(1):19198. doi: 10.1038/s41598-024-69934-y.
The modulation recognition technology for acoustic signals holds significant research importance in signal demodulation and communication signal reconnaissance, serving as a crucial component and key aspect. This paper investigates the modulation recognition technology for acoustic signals (< 20 kHz) from the perspectives of signal preprocessing and feature extraction. Firstly, it selects seven modulation signals 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, and OFDM as recognition targets and systematically compares the effectiveness of four different endpoint detection algorithms in modulation signal recognition. To further enhance the performance of the short-time energy entropy ratio algorithm, this study introduces three different noise reduction algorithms for optimization. Finally, to accurately identify and distinguish between 2 and 4FSK signals, this study optimizes the related algorithms of the cyclic spectrum by using the kurtosis coefficient value Kur of the cyclic spectrum parameter matrix when the cyclic frequency α = 0 to differentiate between these two signals. The results show that at SNR of 4 dB, the proposed modulation recognition algorithm can effectively distinguish between these two signals, achieving a recognition accuracy of over 99%.
声学信号调制识别技术在信号解调与通信信号侦察中具有重要的研究意义，是关键组成部分和关键环节。本文从信号预处理和特征提取的角度研究了（<20kHz）声学信号的调制识别技术。首先，选取2ASK、4ASK、2FSK、4FSK、2PSK、4PSK和OFDM七种调制信号作为识别对象，系统比较了四种不同端点检测算法在调制信号识别中的有效性。为进一步提高短时能量熵比算法的性能，本研究引入三种不同的降噪算法进行优化。最后，为准确识别和区分2FSK和4FSK信号，本研究利用循环频率α=0时循环谱参数矩阵的峭度系数值Kur对循环谱相关算法进行优化，以区分这两种信号。结果表明，在4dB的信噪比下，所提出的调制识别算法能够有效区分这两种信号，识别准确率超过99%。
