Detection and Recognition of Underwater Acoustic Communication Signal Under Ocean Background Noise

Non-cooperative underwater acoustic signal detection and recognition technologies occupy an important position in the marine communication environment. Traditional methods require threshold adjustments based on the environment, making the application less robust. This article addresses the technical challenges of automatic detection and recognition of non-cooperative underwater communication signals. It utilizes two pre-trained models, SSD(Single Shot Detector)-MobileNet-V2-FPNlite(Feature Pyramid Network lite) and EfficientDet, which can simultaneously perform signal classification and automatic drawing of signal bounding boxes on the time-frequency graph. SSD-MobileNet-V2-FPNlite uses the Linear Bottleneck layers and the Inverted Residual structure, which improve the capture of multi-scale representations of signals. EfficientDet performs bidirectional feature fusion to enhance the connections and information flow between signal features. After fine-tuning with simulated data, SSD-MobileNet-V2-FPNlite achieves high detection accuracy on both simulated and real lake test data without transfer learning. EfficientDet also performs well on simulated data and, after transfer learning, shows high accuracy in recognizing and drawing bounding boxes on the lake test data. Both methods exhibit strong generalization ability, feature fewer model parameters, and have lower computational complexity.