  Underwater acoustic target recognition is a difficult task owing to the
intricate nature of underwater acoustic signals. The complex underwater
environments, unpredictable transmission channels, and dynamic motion states
greatly impact the real-world underwater acoustic signals, and may even obscure
the intrinsic characteristics related to targets. Consequently, the data
distribution of underwater acoustic signals exhibits high intra-class
diversity, thereby compromising the accuracy and robustness of recognition
systems.To address these issues, this work proposes a convolution-based mixture
of experts (CMoE) that recognizes underwater targets in a fine-grained manner.
The proposed technique introduces multiple expert layers as independent
learners, along with a routing layer that determines the assignment of experts
according to the characteristics of inputs. This design allows the model to
utilize independent parameter spaces, facilitating the learning of complex
underwater signals with high intra-class diversity. Furthermore, this work
optimizes the CMoE structure by balancing regularization and an optional
residual module. To validate the efficacy of our proposed techniques, we
conducted detailed experiments and visualization analyses on three underwater
acoustic databases across several acoustic features. The experimental results
demonstrate that our CMoE consistently achieves significant performance
improvements, delivering superior recognition accuracy when compared to
existing advanced methods.
