Underwater acoustic target recognition is an important supporting technology for underwater information acquisition and countermeasure. Usually, ship radiated noise is covered by the underwater acoustic background and previous deep learning methods for this task rely on clear and effective acoustic features. We propose a novel network called AMNet to alleviate the problem in this paper. It consists of a multi-branch backbone network coupled with a convolutional attention network. The proposed network is able to obtain the internal features of radiated noise from the time-frequency map of the original data. The convolutional attention network adaptively selects the effective features by weighting them against the global information of the time-frequency map to assist the multi-branch backbone network in classification recognition. Experimental results demonstrate that our model achieves an overall accuracy of 99.4% (2.4% improvement) on the ShipsEar database.

Ship radiated noise is an important information source of underwater acoustic targets, and it is of great significance to the identification and classification of ship targets. However, there are a lot of interference noises in the water, which leads to the reduction of the model recognition rate. Therefore, the recognition results of radiated noise targets are severely affected. This paper proposes a machine learning Dempster–Shafer (ML‐DS) decision fusion method. The algorithm combines the recognition results of machine learning and deep learning. It uses evidence‐based decision‐making theory to realize feature fusion under different neural network classifiers and improve the accuracy of judgment. First, deep learning algorithms are used to classify two‐dimensional spectrogram features and one‐dimensional amplitude features extracted from CNN and LSTM networks. The machine learning algorithm SVM is used to classify the chromaticity characteristics of radiated noise. Then, according to the classification results of different classifiers, a basic probability assignment model (BPA) was designed to fuse the recognition results of the classifiers. Finally, according to the classification characteristics of machine learning and deep learning, combined with the decision‐making of D‐S evidence theory of different times, the decision‐making fusion of radiated noise is realized. The results of the experiment show that the two fusions of deep learning combined with one fusion of machine learning can significantly improve the recognition results of low signal‐to‐noise ratio (SNR) datasets. The lowest fusion recognition result can reach 76.01%, and the average fusion recognition rate can reach 94.92%. Compared with the traditional single feature recognition algorithm, the recognition accuracy is greatly improved. Compared with the traditional one‐step fusion algorithm, it can effectively integrate the recognition results of heterogeneous data and heterogeneous networks. The identification method based on ML‐DS proposed in this paper can be applied in the field of ship radiated noise identification.

By contraposing the signal detection for filter bank multicarrier (FBMC) communications with the underwater acoustic (UWA) channel, this paper analyzes the traditional imaginary interference problem and proposes a deep learning-based method. The neural network with feature extraction and automatic learning ability is employed to replace the demodulation modules to recover transmitted signals without explicit channel estimation and equalization. Sufficient data sets are generated according to the measured channel conditions in Qingjiang river, the optimization of network parameters is finished by constraining cost function in offline training, and the signal detection is carried out directly with the well-trained network in online testing. The system performance of various supervised learning models such as multilayer perceptron (MLP), convolutional neural network (CNN), and bidirectional long short-term memory (BLSTM) network is compared under different data sizes, network parameters, and prototype filters. The simulation results show that the bit error rate (BER) performance of the proposed signal detection is better than that of the classic one, which indicates that deep learning is a promising tool in UWA communication systems.