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Article

Self-Interference Suppression of Unmanned Underwater Vehicle with Vector Hydrophone Array Based on an Improved Autoencoder

by
Jin Fu
1,2,3,
Wenfeng Dong
1,2,3,
Longhao Qiu
1,2,3,*,
Chunpeng Zhao
1,2,3 and
Zherui Wang
1,2,3
1
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China
2
Key Laboratory of Marine Information Acquisition and Security, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China
3
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2023, 11(7), 1358; https://doi.org/10.3390/jmse11071358
Submission received: 29 May 2023 / Revised: 27 June 2023 / Accepted: 29 June 2023 / Published: 3 July 2023
(This article belongs to the Special Issue Underwater Acoustics and Digital Signal Processing)
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Abstract

The self-interference of an unmanned underwater vehicle (UUV) weakens its ability to detect targets of interest. Due to limitations in the size of the sonar array and the complexity of the interference, the performance of existing self-interference suppression methods in practical applications is unsatisfactory. Our research focuses on analyzing the influence of near-field interferences on the sample covariance matrix (SCM) and proposes an interference suppression algorithm based on an improved autoencoder. The proposed algorithm effectively learns the feature distribution of near-field interferences within the covariance domain and reconstructs the pure signal covariance matrix through the cancellation of the near-field interference features. Moreover, the proposed algorithm can meet the requirements of real-time processing and does not require prior knowledge about the positions or propagation of interference. Simulations demonstrate that the proposed algorithm outperforms comparison methods, particularly in scenarios with low signal-to-interference ratios and a limited number of sensors. Furthermore, lake experiments provide additional evidence of the proposed algorithm’s good performance in practical applications.
Keywords: interference suppression; autoencoder; sample covariance matrix; feature reconstruction interference suppression; autoencoder; sample covariance matrix; feature reconstruction

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MDPI and ACS Style

Fu, J.; Dong, W.; Qiu, L.; Zhao, C.; Wang, Z. Self-Interference Suppression of Unmanned Underwater Vehicle with Vector Hydrophone Array Based on an Improved Autoencoder. J. Mar. Sci. Eng. 2023, 11, 1358. https://doi.org/10.3390/jmse11071358

AMA Style

Fu J, Dong W, Qiu L, Zhao C, Wang Z. Self-Interference Suppression of Unmanned Underwater Vehicle with Vector Hydrophone Array Based on an Improved Autoencoder. Journal of Marine Science and Engineering. 2023; 11(7):1358. https://doi.org/10.3390/jmse11071358

Chicago/Turabian Style

Fu, Jin, Wenfeng Dong, Longhao Qiu, Chunpeng Zhao, and Zherui Wang. 2023. "Self-Interference Suppression of Unmanned Underwater Vehicle with Vector Hydrophone Array Based on an Improved Autoencoder" Journal of Marine Science and Engineering 11, no. 7: 1358. https://doi.org/10.3390/jmse11071358

APA Style

Fu, J., Dong, W., Qiu, L., Zhao, C., & Wang, Z. (2023). Self-Interference Suppression of Unmanned Underwater Vehicle with Vector Hydrophone Array Based on an Improved Autoencoder. Journal of Marine Science and Engineering, 11(7), 1358. https://doi.org/10.3390/jmse11071358

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