A Multi-target Underwater Acoustic Signals Denoising Method Based on Wavelet

Fang, YuChen; He, Qinyuan; Bai, Libing; Yu, Huapeng; Tian, ShaoChun; Wang, Xiang

doi:10.1109/cecit58139.2022.00063

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…In order to address the challenge of removing ocean reverberation noise, this paper proposes a method that involves converting the noise into Gaussian noise through function conversion. [15] Next, the high and low frequency components are decomposed using the CEEMDAN algorithm, which incorporates the cross-correlation function. Then, the improved wavelet threshold algorithm is applied to eliminate the noise.…”

Section: Algorithm Basic Flowmentioning

confidence: 99%

Research on Noise Reduction Method of Underwater Acoustic Signal Based on CEEMDAN Decomposition-Improved Wavelet Threshold

Jiang,

Zhang,

et al. 2024

J. Phys.: Conf. Ser.

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Due to the complex noise in the ocean environment, the signal-to-noise ratio of the hydrophone receiving signal is often low, making subsequent signal processing difficult. To solve this problem, this paper proposes using CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) decomposition algorithm combined with an improved wavelet threshold algorithm to process the signal, and obtain the reconstructed signal after denoising. In this method, the noise-containing signal is transformed by the function and decomposed into multiple natural mode components with frequencies ranging from high to low using the CEEMDAN algorithm. The correlation component and the non-correlation component are then determined using the cross-correlation function. The non-correlated compinents are denoised using the improved wavelet threshold method and the denoised signal is obtained by reconstructing the signal. Experimental results show that this method can improve the performance of underwater acoustic signal denoising.

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Section: Algorithm Basic Flowmentioning

confidence: 99%

Research on Noise Reduction Method of Underwater Acoustic Signal Based on CEEMDAN Decomposition-Improved Wavelet Threshold

Jiang,

Zhang,

et al. 2024

J. Phys.: Conf. Ser.

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“…With the improvement of wavelet analysis technology, its excellent nonstationary signal analysis ability has received attention in the underwater acoustic field; so, it has been widely used in the analysis of radiation noise. Wavelet analysis is used to decompose underwater acoustic signals, and the wavelet coefficients extracted from the respective signals can reflect the characteristics of the target [6], which can not only improve the accuracy of the target recognition but also provide a new idea for signal denoising. The combination of empirical mode decomposition (EMD) and wavelet decomposition is used to remove noise from underwater acoustic targets, resulting in signals with a high ratio of signal to noise.…”

Section: Introductionmentioning

confidence: 99%

A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature

Du,

Wang,

et al. 2024

Remote Sensing

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With the aim of solving the problem of identifying complex underwater acoustic targets using a single signal feature in the Time–Frequency (TF) feature, this paper designs a method that recognizes the underwater targets based on the Delay-Doppler joint feature. First, this method uses symplectic finite Fourier transform (SFFT) to extract the Delay-Doppler features of underwater acoustic signals, analyzes the Time–Frequency features at the same time, and combines the Delay-Doppler (DD) feature and Time–Frequency feature to form a joint feature (TF-DD). This paper uses three types of convolutional neural networks to verify that TF-DD can effectively improve the accuracy of target recognition. Secondly, this paper designs an object recognition model (TF-DD-CNN) based on joint features as input, which simplifies the neural network’s overall structure and improves the model’s training efficiency. This research employs ship-radiated noise to validate the efficacy of TF-DD-CNN for target identification. The results demonstrate that the combined characteristic and the TF-DD-CNN model introduced in this study can proficiently detect ships, and the model notably enhances the precision of detection.

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“…In shallow water condition, underwater acoustics signals contain noise that originated from different sources such as reflection of surfaces, interference from fish or acoustics signal generated by ships. These facts explain that there are many techniques develop to reduce noise such as deep learning approach [1], value decomposition algorithm [2], wavelet transform [3], or neural networks [4]. These techniques are chosen to cope challenges in applying underwater acoustics signal for communications such as frequency-dependent attenuation [5], short range of communication [6], and very low bandwidth [7].…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Intensity Analysis using Noise Assisted-MEMD with Varying Distances

Fajarwati,

Feriadi,

Widjiati

2023

Jurnal Teknik Elektro

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With current developments, underwater communication using acoustic signals is widely used. Many things need to be prepared to support a reliable underwater communication system, such as taking measurements in a test tank to find out the correct measurement configuration. Underwater acoustic intensity measurements, which are detailed in this paper, are performed in the test tank using distance variation schemes. Measurements were made at various distances of 4, 10, 20, and 50 meters from the signal source. The hydrophone that was used has a sensitivity of -180 dB re 1V/µPa. The hydrophone was placed at a depth of 2 meters below the surface of the water in the test tank, which divided the test tank depth in half to ensure that reflections from the bottom and the surface were kept to a minimum. However, the problem is that there are noisy signals at different frequencies. This paper proposes a method using Noise Assisted - Multivariate Empirical Mode Decomposition (NA-MEMD) to decompose the signal and then calculate the sound intensity. The result shows that an increase in the distance between the transmitter and receiver, also causes a change in the intensity with an average change of 0.467 dB/meter. It is concluded that the NA-MEMD approach was shown to be successful in decomposing the intended signal from the noise to equalize the quality of the signal received at different distances, and the correlation between intensity value and change in distance is resilient, with a correlation value of 0.98, indicating a very strong correlation.

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A Multi-target Underwater Acoustic Signals Denoising Method Based on Wavelet

Cited by 3 publications

References 12 publications

Research on Noise Reduction Method of Underwater Acoustic Signal Based on CEEMDAN Decomposition-Improved Wavelet Threshold

Research on Noise Reduction Method of Underwater Acoustic Signal Based on CEEMDAN Decomposition-Improved Wavelet Threshold

A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature

Underwater Acoustic Intensity Analysis using Noise Assisted-MEMD with Varying Distances

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