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Sensors 2018, 18(12), 4318; https://doi.org/10.3390/s18124318 (registering DOI)

Underwater Acoustic Target Recognition Based on Supervised Feature-Separation Algorithm

Key Laboratory of Underwater Acoustic Communication and Marine Information Technology (Xiamen University) Ministry of Education, Xiamen University, Xiamen 361005, China
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Received: 30 October 2018 / Revised: 28 November 2018 / Accepted: 2 December 2018 / Published: 7 December 2018
(This article belongs to the Special Issue Underwater Sensor Networks: Applications, Advances and Challenges)
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Abstract

For the purpose of improving the accuracy of underwater acoustic target recognition with only a small number of labeled data, we proposed a novel recognition method, including 4 steps: pre-processing, pre-training, fine-tuning and recognition. The 4 steps can be explained as follows: (1) Pre-processing with Resonance-based Sparsity Signal Decomposition (RSSD): RSSD was firstly utilized to extract high-resonance components from ship-radiated noise. The high-resonance components contain the major information for target recognition. (2) Pre-training with unsupervised feature-extraction: we proposed a one-dimensional convolution autoencoder-decoder model and then we pre-trained the model to extract features from the high-resonance components. (3) Fine-tuning with supervised feature-separation: a supervised feature-separation algorithm was proposed to fine-tune the model and separate the extracted features. (4) Recognition: classifiers were trained to recognize the separated features and complete the recognition mission. The unsupervised pre-training autoencoder-decoder can make good use of a large number of unlabeled data, so that only a small number of labeled data are required in the following supervised fine-tuning and recognition, which is quite effective when it is difficult to collect enough labeled data. The recognition experiments were all conducted on ship-radiated noise data recorded using a sensory hydrophone. By combining the 4 steps above, the proposed recognition method can achieve recognition accuracy of 93.28%, which sufficiently surpasses other traditional state-of-art feature-extraction methods. View Full-Text
Keywords: deep learning; autoencoder-decoder; Resonance-based Sparsity Signal Decomposition; target recognition; ship-radiated noise; feature-extraction; feature-separation deep learning; autoencoder-decoder; Resonance-based Sparsity Signal Decomposition; target recognition; ship-radiated noise; feature-extraction; feature-separation
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Ke, X.; Yuan, F.; Cheng, E. Underwater Acoustic Target Recognition Based on Supervised Feature-Separation Algorithm. Sensors 2018, 18, 4318.

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