Acoustic barcode based on the acoustic scattering characteristics of underwater targets

Zhou, Fulin; Jun, Fan; Wang, Bin; Zhou, Yue; Huang, Jinfeng

doi:10.1016/j.apacoust.2021.108607

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…In addition, there are studies on underwater highresolution visual reconstruction, which can even convey high-definition images of a target, with the premise of needing to recognize the device and in close proximity to the target to be detected. When the distance is farther away from each other, it is necessary to rely on the detection device to determine the target's orientation in advance, and then perform the reconstruction of the image after being close to the target [3,4]. In contrast to electromagnetic and light waves, sound is the only form of energy found so far in the oceans that can travel over long distances, so there is still a demand for hydroacoustic technology for long-range ocean target exploration.…”

Section: Introductionmentioning

confidence: 99%

Design and Algorithm Integration of High-Precision Adaptive Underwater Detection System Based on MEMS Vector Hydrophone

Liu,

Jing,

Zhang

et al. 2024

Micromachines

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Real-time DOA (direction of arrival) estimation of surface or underwater targets is of great significance to the research of marine environment and national security protection. When conducting real-time DOA estimation of underwater targets, it can be difficult to extract the prior characteristics of noise due to the complexity and variability of the marine environment. Therefore, the accuracy of target orientation in the absence of a known noise is significantly reduced, thereby presenting an additional challenge for the DOA estimation of the marine targets in real-time. Aiming at the problem of real-time DOA estimation of acoustic targets in complex environments, this paper applies the MEMS vector hydrophone with a small size and high sensitivity to sense the conditions of the ocean environment and change the structural parameters in the adaptive adjustments system itself to obtain the desired target signal, proposes a signal processing method when the prior characteristics of noise are unknown. Theoretical analysis and experimental verification show that the method can achieve accurate real-time DOA estimation of the target, achieve an error within 3.1° under the SNR (signal-to-noise ratio) of the X channel of −17 dB, and maintain a stable value when the SNR continues to decrease. The results show that this method has a very broad application prospect in the field of ocean monitoring.

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Section: Introductionmentioning

confidence: 99%

Design and Algorithm Integration of High-Precision Adaptive Underwater Detection System Based on MEMS Vector Hydrophone

Liu,

Jing,

Zhang

et al. 2024

Micromachines

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“…The earth is abundant in marine resources. With the advancements in human productivity and technology, there is a growing interest in utilizing, developing, and protecting the ocean [ 1 , 2 , 3 , 4 ]. In underwater signal transmission, acoustic signals serve as the primary carriers of information.…”

Section: Introductionmentioning

confidence: 99%

Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network

Li,

Gu,

Fan

2024

Sensors

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This study introduces a novel nonlinear dynamic analysis method, known as beluga whale optimization–slope entropy (BWO-SlEn), to address the challenge of recognizing sea state signals (SSSs) in complex marine environments. A method of underwater acoustic signal recognition based on BWO-SlEn and one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, particle swarm optimization–slope entropy (PSO-SlEn), BWO-SlEn, and Harris hawk optimization–slope entropy (HHO-SlEn) were used for feature extraction of noise signal and SSS. After 1D-CNN classification, BWO-SlEn were found to have the best recognition effect. Secondly, fuzzy entropy (FE), sample entropy (SE), permutation entropy (PE), and dispersion entropy (DE) were used to extract the signal features. After 1D-CNN classification, BWO-SlEn and 1D-CNN were found to have the highest recognition rate compared with them. Finally, compared with the other six recognition methods, the recognition rates of BWO-SlEn and 1D-CNN for the noise signal and SSS are at least 6% and 4.75% higher, respectively. Therefore, the BWO-SlEn and 1D-CNN recognition methods proposed in this paper are more effective in the application of SSS recognition.

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Effects of internal solitary waves on three-dimensional sound propagation and DOA estimation in the South China Sea

Li,

Huang,

Shi

et al. 2023

Applied Acoustics

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Acoustic barcode based on the acoustic scattering characteristics of underwater targets

Cited by 3 publications

References 22 publications

Design and Algorithm Integration of High-Precision Adaptive Underwater Detection System Based on MEMS Vector Hydrophone

Design and Algorithm Integration of High-Precision Adaptive Underwater Detection System Based on MEMS Vector Hydrophone

Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network

Effects of internal solitary waves on three-dimensional sound propagation and DOA estimation in the South China Sea

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