A technique of suppressing towed ship noise

Li, Yang; Sun, Changyu; Yu, Huabing; Wang, Lei

doi:10.1109/icspcc.2011.6061631

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Self-interference suppression poses a critical challenge for UUVs, leading researchers to explore various techniques to address this issue. These techniques can be categorized into adaptive interference cancellation [2,3], focused-beamforming-based interference cancellation [4][5][6], and spatial-filtering-based interference cancellation [7][8][9][10][11]. Gao et al [3] have employed an adaptive filtering method to cancel correlated noise of sonar arrays by extracting reference noise with a multi-channel difference method.…”

Section: Introductionmentioning

confidence: 99%

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

Dong

Qiu

et al. 2023

JMSE

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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.

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Section: Introductionmentioning

confidence: 99%

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

Dong

Qiu

et al. 2023

JMSE

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“…Later Li. et al [15] proposed a combination of IBF and CLEAN algorithm, to improve the results of inverse beamforming, by removing sidelobes. Jia et al [16] presents a comparison of null steered beamformer and IBF.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Tow Ship Noise Cancellation Using Deep Regression Neural Network

Remadevi

Abraham

Rajesh

et al. 2022

AiMT

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This paper investigates the problem of cancellation of noise generated by own platform in shallow water scenario. In the case of underwater acoustics, the target signal detection and tracking in the presence of tow ship noise is a challenging task. A computationally intensive technique is necessary for tow ship noise suppression. In this paper, an algorithm using deep regression neural network (DRNN) along with minimum variance distortionless response (MVDR) beamformer is presented for tow ship noise cancellation. Nine DRNN’s each with different weight initialization techniques and activation functions are designed for effective tow ship noise cancellation. The designed DRNNs is tested using the simulated data and further validated using the real data collected during the trials from Arabian Sea.

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