Abstract
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%.