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Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

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Ray-based blind deconvolution with maximum kurtosis phase correction.

Seunghyun Yoon¹, Haesang Yang¹, Woojae Seong¹

¹Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul 08826, Republic of Korea.

The Journal of the Acoustical Society of America

|July 1, 2022

Summary

This summary is machine-generated.

This study introduces a new method, ray-based blind deconvolution with maximum kurtosis phase correction (RBD-MKPC), to improve underwater acoustic signal estimation. The technique enhances accuracy by correcting phase errors in source waveform estimation.

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Area of Science:

Underwater acoustics
Signal processing

Background:

Ray-based blind deconvolution (RBD) estimates underwater source waveforms and channel impulse responses (CIRs).
Beamforming in RBD can introduce phase errors due to factors like low sampling and array deformation.

Purpose of the Study:

To enhance the performance of ray-based blind deconvolution (RBD) by addressing phase errors in source phase estimation.
To introduce a novel phase correction method utilizing channel impulse response (CIR) impulsiveness.

Main Methods:

Developed ray-based blind deconvolution with maximum kurtosis phase correction (RBD-MKPC).
Employed kurtosis to measure CIR impulsiveness for phase correction optimization.
Tested RBD-MKPC using broadband high-frequency pulses in a shallow-water experiment with a vertical line array.

Main Results:

RBD-MKPC demonstrated improved estimation performance for underwater acoustic signals.
The proposed method exhibited superior convergence speed compared to other initialization techniques.
Enhanced accuracy in estimating both source waveforms and channel impulse responses was observed.

Conclusions:

The proposed maximum kurtosis phase correction effectively improves RBD performance in underwater environments.
RBD-MKPC offers a robust and efficient solution for underwater acoustic signal processing.
The method shows significant advantages in convergence and estimation accuracy.