融合多模态对比学习的仿嘀嗒声水声仿生通信信号识别方法OA

To enhance the recognition performance of short-sample click-like bionic hydroacoustic communication signals in complex underwater environments,a recognition method based on fused mul-timodal contrastive learning is proposed.Firstly,by incorporating time-frequency and fused feature mo-dalities,discriminative features are directly extracted from fine-grained signal structures,reducing reli-ance on long-sequence statistical stability.Secondly,the samples of the same class are pulled together and those of different classes are pushed apart to the model to focus on robust features that are insensi-tive to multipath interference using the contrestive learning mechanism.Finally,the introduction of a text modality provides semantic constraints for feature learning,guiding the model to capture category-related essential attributes.Simulation and lake trial results demonstrate that the proposed method sig-nificantly outperforms existing algorithms based on time-delay statistics and statistical-convolutional neural networks under short-sample and multipath hydroacoustic channel conditions.Specifically,the lake trial achieves a recognition accuracy of 88.70%with a short sample length of 0.7 s.