• Journal of the Korea Society for Simulation
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• v.25 no.3
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• pp.15-28
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• 2016

It is necessary to analyze underwater acoustics channel(UAC) modeling and simulation for underwater weapon system development and acquisition. In order to analyze UAC, there are underwater acoustics propagation numerical analysis models(Ray theory, Parabolic equation, Normal-mode, Wavenumber integration). However, If these models are used for multiple frequency signal analysis, they are inaccurate to calculate result of analysis effectiveness and restricted for signal processing and analysis. In this paper, to overcome this problem, we propose simple/multiple frequency signal analysis model of the Pseudospectral Time-Domain Method synthetic environment UAC applied to underwater environment noise model as like as realistic underwater environment. In order to confirm the validation of the model, we performed the 9 scenarios simulation(4 scenarios of single frequency signal, 4 scenarios of multiple frequency signal, 1 scenario of single/multiple frequency signal like submarine radiated noise) for validation and confirmed the validation of this model through the simulation model.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.765-771
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• 2019

With the increase in the speed of ships and the size of ocean structures, the importance of flow noise has become increasingly critical in meeting regulatory standards. However, unlike active investigations in aeroacoustics fields for airplanes and trains, which are based on acoustic analogy methods for tonal and broadband frequency noise, only the discrete blade passing frequency noise from propellers is considered in marine fields. In this study, prediction methods for broadband noise in marine propellers and underwater appendages are investigated using FW-H Formulation1B, which can consider the mechanism of primary noise generation of trailing edge noise. The original FW-H Formulation 1B is based on the pressure correlation function tolackitsgeneralityandaccuracy. To overcome these limitations, wall-pressure spectrum models are adopted to improve the generality in fluid mediums. The comparison of the experimental results obtained in air reveals that the proposed model exhibits a higher accuracy within 5 dB. Furthermore, the prediction procedures for broadband noise for hydrofoils are established, and the estimation of broadband noise is conducted based on the results of the computational fluid dynamics.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.1
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• pp.38-44
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• 2011

Because of the complexity of the underwater environment, the communication has difficulties that can differ significantly from those in air, The signal is degraded by many random noises. Furthermore, the limit of the bandwidth is a big issue in underwater communication. Therefore, the array signal processing can be adapted to improve the signal-to-noise ratio. In this paper, we propose a potential method for underwater communication based upon source tracking techniques. Also, a new tracking model by using a multi-array sonar and detail of the multi-array sonar configuration are shown in this paper. The experiment results demonstrated the receiver configuration is very potential to solve communication problems, especially in the underwater environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.311-318
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• 2021

Underwater, signals are transmitted by sound waves. Sound waves are transmitted through a multipath, either directly or through reflection, due to the variety of underwater environmental characteristics. In such diverse and complex underwater environments, tests must be conducted to determine the extent of the hazard from the survivability and pitfalls of submarines by measuring the underwater radiated noise. Usually, the sound source level measurement of underwater radiated noise should be made within the closest point (CPA: Closest Point of Approach) ± a few meters between the measurement sensor and the submarine. In this study, FDOA and TDOA methods were proposed to estimate the underwater source range. A simulation based on the underwater channel model confirmed the performance of the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.285-292
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• 2024

When calculating sonar detection probability, underwater acoustic uncertainty is assumed to be normal distributed with a standard deviation of 8 dB to 9 dB. However, due to the variability in experimental areas and ocean environmental conditions, predicting detection performance requires accounting for underwater acoustic uncertainty based on ocean experimental data. In this study, underwater acoustic uncertainty was determined using measured mid-frequency (2.3 kHz, 3 kHz) noise level and transmission loss data collected in the shallow water of the East Sea. After calculating the predictable probability of detection reflecting underwater acoustic uncertainty based on ocean experimental data, we compared it with the conventional detection probability results, as well as the predictable probability of detection results considering the uncertainty of the Rayleigh distribution and a negatively skewed distribution. As a result, we confirmed that differences in the detection area occur depending on each underwater acoustic uncertainty.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.19-28
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• 2024

This paper proposes a method to improve the performance of ship identification through lofargram analysis of ship noise by applying the Hough Transform to a Convolutional Neural Network (CNN) model. When processing the signals received by a passive sonar, the time-frequency domain representation known as lofargram is generated. The machinery noise radiated by ships appears as tonal signals on the lofargram, and the class of the ship can be specified by analyzing it. However, analyzing lofargram is a specialized and time-consuming task performed by well-trained analysts. Additionally, the analysis for target identification is very challenging because the lofargram also displays various background noises due to the characteristics of the underwater environment. To address this issue, the Hough Transform is applied to the lofargram to add lines, thereby emphasizing the tonal signals. As a result of identification using CNN models on both the original lofargrams and the lofargrams with Hough transform, it is shown that the application of the Hough transform improves lofargram identification performance, as indicated by increased accuracy and macro F1 scores for three different CNN models.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.930-938
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• 2018

Underwater noise radiated from submarines is directly related to the probability of being detected by the sonar of an enemy vessel. Therefore, minimizing the noise of a submarine is essential for improving survival outcomes. For modern submarines, as the speed and size of a submarine increase and noise reduction technology is developed, interest in flow noise around the hull has been increasing. In this study, a noise analysis technique was developed to predict flow noise generated around a submarine shape considering the free surface effect. When a submarine is operated near a free surface, turbulence-induced noise due to the turbulence of the flow and bubble noise from breaking waves arise. First, to analyze the flow around a submarine, VOF-based incompressible two-phase flow analysis was performed to derive flow field data and the shape of the free surface around the submarine. Turbulence-induced noise was analyzed by applying permeable FW-H, which is an acoustic analogy technique. Bubble noise was derived through a noise model for breaking waves based on the turbulent kinetic energy distribution results obtained from the CFD results. The analysis method developed was verified by comparison with experimental results for a submarine model measured in a Large Cavitation Tunnel (LCT).

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.78-88
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• 2024

Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.

• The Journal of the Acoustical Society of Korea
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• v.32 no.6
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• pp.455-471
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• 2013

Active sonar has been commonly used to detect targets existing in the shallow water. When a signal is transmitted and returned back from a target, it has been distorted by various properties of acoustic channel such as multipath arrivals, scattering from rough sea surface and ocean bottom, and refraction by sound speed structure, which makes target detection difficult. It is therefore necessary to consider these channel properties in the target signal simulation in operational performance system of active sonar. In this paper, a monostatic active sonar system is considered, and the target echo, reverberation, and ambient noise are individually simulated as a function of time, and finally summed to simulate a total received signal. A 3-dimensional highlight model, which can reflect the target features including the shape, position, and azimuthal and elevation angles, has been applied to each multipath pair between source and target to simulate the target echo signal. The results are finally compared to those obtained by the algorithm in which only direct path is considered in target signal simulation.