Active sonar target recognition method based on multi-domain transformations and attention-based fusion network

The classification and recognition of underwater targets by an active sonar system remain challenging and complex. Traditional methods have limited classification performance in time and spatially varying ocean channels. An active sonar target recognition method is proposed based on multi-domain transformations and an attention-based fusion network. Initially, the active target echo undergoes time-frequency analysis, auditory signal processing, and matched filtering to represent target attributes in joint spatial-time-frequency domains. Subsequently, multiple attention-based fusion models fuse the multi-domain transformations either early or late in the processing stages. An attention module further enhances significant feature channels through adaptive weight assignment. Experiment results demonstrate that the recognition accuracy of active sonar echoes using multi-domain transformations improves significantly compared to that of single-domain methods, with an increase of up to 10.5%. The incorporation of multiple transformation domains provides complementary information about the target, thereby enhancing the network's representation ability, especially with limited data samples. Furthermore, the findings indicate that feature fusion of multiple transformations in a high-level feature space yields more informative and effective results for active sonar echoes compared to low-level feature spaces.