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| A stack sparse denoising autoencoder-based neural network approach for ship radiated noise target recognition |
| JU Donghao1,2,3，LI Yu1,3，WANG Yujie1,2,3，ZHANG Chunhua1,3 |
1. Institute of Acoustics, Chinses Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100039, China;
3. Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing, Chinese Academy of Science, Beijing 100190, China |
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Abstract The traditional feature extraction algorithm relies on the prior knowledge of the algorithm designer. Because it does not have the advantage of highlighting big data, the classification accuracy in practical application is poor and the generalization ability for different application scenarios is also obviously insufficient. In this paper, the deep learning algorithm is used for feature extraction of ship radiated noise, and a large number of classless data are fully utilized. The stack sparse self-encoder algorithm is used to train the feature extraction neural network, and the Softmax classifier algorithm is used to fine-tune the parameters of the neural network by using class-based data. By comparing with principal component analysis algorithm, linear discriminant analysis algorithm and local linear embedding algorithm, it can be seen that the ssdae-softmax algorithm proposed in this paper can extract more discriminative features from ship radiated noise and improve the classification and recognition accuracy to some extent.
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Received: 27 July 2020
Published: 28 December 2021
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