Modulation Recognition of Underwater Acoustic Signals Based on Ghost-Former

In order to address the issues of high computational complexity, low accuracy, and the cumbersome manual feature extraction steps involved in traditional algorithms for modulating and recognizing underwater acoustic signals, this paper proposes a modulation recognition method based on Ghost-former for hydroacoustic signals. This approach harnesses the advantages of Ghost Net in generating more feature maps through cost-effective operations, along with a two-way bridge for global interaction. Experimental results with simulated signals demonstrate that this method achieves excellent recognition performance even with relatively low FLOPS.