Open set recognition of underwater acoustic targets based on GRU-CAE collaborative deep learning network
Honghui Yang, Kaifeng Zheng, Junhao Li
Abstract
Aiming at the problem of underwater acoustic target open set recognition, a new recognition method based on deep learning and template matching is proposed in this paper. The proposed method includes three stages: feature extraction based on GRU-CAE collaborative deep learning network, template establishment and template matching. Firstly, the GRU-CAE collaborative deep learning network is proposed to extract the deep collaborative features. The GRU-CAE cooperative deep learning network combines the advantages of CAE network in extracting spatial information of spectrogram and GRU network in extracting temporal structure features of spectrogram. Then, the feature template of each class of underwater acoustic target is constructed by finding an optimal vector in Euclidean space. Finally, the distance between the deep collaborative features of the test set and templates are calculated to distinguish the category of the samples. The experimental data of 5 kinds of underwater acoustic targets are used to verify the recognition performance of the proposed method. The results show that the deep collaborative features have excellent class separability, and the recognition accuracy rate of open set recognition can reach 82.21%. Compared with the other 3 open set recognition methods, the proposed method has the highest recognition accuracy rate.