Frequency Domain Based Approach for Denoising of Underwater Acoustic Signal Using EMD

Veeraiyan, Vijayabaskar; Rajendran, V.; Philip, Mathews M.

doi:10.1515/jisys-2012-0021

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…Pixel is defined as a small unit of image. The most important parameter that corrupts the image resolution is the ambient noise [16], Scattering, Reflection of the surface and the ocean floor. Reflected signal from the surface perfectly gets diverted back into the water.…”

Section: Automatic Target Recognition Problem In Sonar Imagesmentioning

confidence: 99%

Super resolution enhancement of underwater sonar images

Kumudham

Rajendran

2019

SN Appl. Sci.

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Image resolution is determined by pixels per square inches. Pixel is defined as a small unit of an image. The resolution improvement of the sonar images still remains a problem and it is compensated by the interpolation techniques. This work helps to improve the resolution of the sonar images and thereby it makes target object recognition with higher accuracy. The super resolution algorithm of the proposed work uses sparse representation for enhancing the resolution. This gives sharper edges and textures to highlight the boundaries of the target object. The low resolution image is converted into high resolution image that is of high pixel density. The peak signal to noise ratio and Structural Similarity Index is the performance metric employed to assess the different interpolation techniques for resolution enhancement.

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Section: Automatic Target Recognition Problem In Sonar Imagesmentioning

confidence: 99%

Super resolution enhancement of underwater sonar images

Kumudham

Rajendran

2019

SN Appl. Sci.

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“…EMD and its improved algorithm provides a new idea for noise reduction of ship-radiated noise, which is "decompose the signal first and then reduce the noise". Among them, typical methods are EMD and wavelet threshold method [19], noise adaptive EMD and correlation coefficient method [20], CEEMDAN, mutual information, permutation entropy (PE) and wavelet threshold method [21], UPEMD and amplitude-aware permutation entropy [22]. However, EMD and its improved algorithm belong to the empirical decomposition algorithm and lack a strict mathematical theoretical basis.…”

Section: Introductionmentioning

confidence: 99%

A New Denoising Method for Underwater Acoustic Signal

Yang

2020

IEEE Access

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In recent years, the rapid development of marine science has put forward higher and higher requirements for the processing of ship-radiated noise signal. Ship-radiated noise is the noise signal generated by the vibration of various mechanical equipment or the movement of the hull and radiated into the sea when the ship is traveling. Ship-radiated noise signal contains a large number of time-varying, nonlinear and non-stationary components. The denoising processing of ship-radiated noise is the most critical part of underwater acoustic signal processing. In order to more effective reduce the noise of the ship-radiated noise signal, a new denoising method for underwater acoustic signal based on mutual information variational mode decomposition (MIVMD), multivariate multiscale dispersion entropy (mvMDE), and lift wavelet threshold (LWTD) and Savitzky Golay filter (S-G filter), named MIVMD-mvMDE-LWTD-SG, is proposed. Firstly, MIVMD is used to decompose the original signal into n subsignals. Secondly, the mvMDE value of each sub-signal is calculated, and the n sub-signals are divided into high-frequency components and low-frequency components according to the threshold. Then, S-G filter and LWTD method are used to reduce the noise of low-frequency components and high-frequency components respectively. Finally, the low-frequency components and high-frequency components after the denoising processing are reconstructed to obtain the denoising signal. In order to verify the effectiveness of the proposed method, the proposed method is used to reduce the noise of chaotic signal under different signalto-noise ratios (SNR), and compared with the EMD-mvMDE-LWTD and MIVMD-mvMDE-LWTD method. The results show that the proposed method can effective remove the noise in the chaotic signal, better distinguish the adjacent trajectories in the phase space, approximate the real chaotic attractor trajectory, and better retain the useful information in the chaotic signal. The proposed method is further applied to the actual ship-radiated noise signal, and the experimental analysis shows its effectiveness, which lays a solid foundation for further prediction and detection. INDEX TERMS Mutual information variational mode decomposition; multivariate multiscale dispersion entropy; lift wavelet threshold; chaotic signal; denoising

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Exploiting Compress Sensing in Training of Deep Neural Network for Self-Noise Cancellation in Underwater Acoustics

Kumar,

Nathwani

2024

J Sign Process Syst

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Frequency Domain Based Approach for Denoising of Underwater Acoustic Signal Using EMD

Cited by 9 publications

References 12 publications

Super resolution enhancement of underwater sonar images

Super resolution enhancement of underwater sonar images

A New Denoising Method for Underwater Acoustic Signal

Exploiting Compress Sensing in Training of Deep Neural Network for Self-Noise Cancellation in Underwater Acoustics

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