Underwater acoustic target recognition based on spectrum component analysis of ship radiated noise

Underwater acoustic target recognition based on ship radiated noise has always been a challenging task due to the complexity of underwater environment and the antagonism of targets. An underwater acoustic target recognition network based on Ship radiated Noise spectrum component Analysis (SNANet) is proposed in this paper by extracting the spectrum features of each component in different frequency bands, which improves the recognition accuracy as compared with existing end-to-end recognition methods. Adaptive weight based on forward weight and backward weight is used to fuse main and auxiliary features. Experiments have demonstrated that SNANet surpasses currently existing models in performance on the public dataset DeepShip.