Underwater Acoustic Target Recognition with Fusion Feature

Pengyuan Qi, Jianguo Sun, Yunfei Long, Liguo Zhang, Tianye

Abstract: For the underwater acoustic targets recognition, it is a challenging task to provide good classification accuracy for underwater acoustic target using radiated acoustic signals. Generally, due to the complex and changeable underwater environment, when the difference between the two types of targets is not large in some sensitive characteristics, the classifier based on single feature training cannot output correct classification. In addition, the complex background noise of target will also lead to the degradation of feature data quality. Here, we present a feature fusion strategy to identify underwater acoustic targets with one-dimensional Convolutional Neural Network. This method mainly consists of three steps. Firstly, considering the phase spectrum information is usually ignored, the Long and Short-Term Memory (LSTM) network is adopted to extract phase features and frequency features of the acoustic signal in the real marine environment. Secondly, for leveraging the frequency-based features and phase-based features in a single model, we introduce a feature fusion method to fuse the different features. Finally, the newly formed fusion features are used as input data to train and validate the model. The results show the superiority of our algorithm, as compared with the only single feature data, which meets the intelligent requirements of underwater acoustic target recognition to a certain extent.