• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.612-613
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• 2008

• The Journal of the Acoustical Society of Korea
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• v.23 no.4
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• pp.288-295
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• 2004

Detection and localization of a slow and quiet target in shallow water environments is a challenging problem for which it is well known that snapshot is deficient because of a fast and strong interferer. This paper presents waveguide interference filtering technique that mitigate strong interferer problems in adaptive matched field processing. MCM (multiple constraint method) based on NDC (null direction constraint) has been proposed for new spatial interferer filter. MCM-NDC using replica force a interferer component to be filtered through CSDM (cross-spectral density matrix). This filtering have an effect on sidelobe reduction and restoring of signal gain of a quiet target. This technique was applied to a simulation on Pekeris waveguide and vertical array data from MAPLE03 (matched acoustic properties and localization experiment) in the East Sea and was shown to improve SBNR (signal-to-background-and-noise ratio) over the standard MVDR (minimum-variance distortionless response) and NSP (null space projection) technique.

• The Journal of the Acoustical Society of Korea
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• v.31 no.4
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• pp.205-213
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• 2012

Active acoustic array signal was measured in the East Sea and the South sea and spatial coherence was analyzed. The measurement of ambient noise, target reflection signal, sea surface backscattering signals took place including environmental measurements of sea wind, and vertical temperature profiles. The spatial coherence of ambient noise was lower than that of target reflection signal in the South Sea. The spatial coherence of target reflection signal was above 0.5 over all array length. The spatial coherence of sea surface backscattering signal was higher in high incident angle. The maximum non-dimensional array length was 3.0 ($26^{\circ}$) and 3.5 ($32^{\circ}$) to have spatial coherence above 0.5 in the East Sea. To find a design criteria for array configuration and array performance, more measurements of temporal and spatial coherence will be needed continuously in the future.

• The Journal of the Acoustical Society of Korea
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• v.36 no.3
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• pp.218-227
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• 2017

In this paper, an algorithm to calculate both bearing and distance error for target detection and localization is proposed using the Cramer Rao lower bound to estimate the minium variance of their error in DOA (Direction Of Arrival) estimation. The performance of arrays in detection and localization depends on the accuracy of DOA, which is affected by a variation of SNR (Signal to Noise Ratio). The SNR is determined by sonar parameters such as a SL (Source Level), TL (Transmission Loss), NL (Noise Level), array shape and beam steering angle. For verification of the suggested method, a Monte Carlo simulation was performed to probabilistically calculate the bearing and distance error according to the SNR which varies with the relative position of the target in space and noise level.

• The Journal of the Acoustical Society of Korea
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• v.37 no.3
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• pp.139-146
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• 2018

Monostatic sonar systems localize targets using the time information of pulse transmission and receipt. Whereas, in asynchronous bistatic sonar systems, receivers need to detect direct blast to localize targets, since a source doesn't share pulse information with receivers. In this paper, we propose a direct blast detection algorithm, which estimates PRI (Pulse Repetition Interval) of direct blast and adaptive thresholds. Experimental results show the proposed algorithm has robust direct blast detection performance in the environment where strong background noise and target signal exist.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.120-127
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• 2019

Passive source depth discrimination has been studied for decades since the source depth can be used for discriminating whether the target is near the surface or submerged. In this thesis, an algorithm for source depth discrimination is proposed based on CIR (Channel Impulse Response) from target-radiated noise (or signal). In order to extract CIR without a known source signal, Ray-based blind deconvolution is used. Subsequently, intersections of CIR pattern, which is characterized by ray arrival time difference, is utilized for discriminating source depth. The proposed algorithm is demonstrated through numerical simulation in ocean waveguide, and verified via the experimental data.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• 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.

• 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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.6
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• pp.440-446
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• 2015

Acoustic signal is emitted from a vessel and received by a cylindrical array sensor at some distance from the vessel. Acoustic signal is the source for a cylindrical array sensor which is designed to detect the acoustic signal. Cylindrical array sensors seldom have an ideal hydrodynamic shape and are not sufficiently robust to survive without some protection and they are normally housed in a sonar dome. Reflected signals by some structure inside a sonar dome make unwanted signals. Therefore, an acoustic baffle is used to minimize unwanted signals. The performance of the acoustic baffles can be determined from the acoustic numerical analysis at the design stage. In this study, finite element method was used to analyze the acoustic field around the cylindrical array sensor and baffle effects. The baffle performance can be defined the echo reduction. To show the baffle performance, the specimens were made for pulse tube test and echo reductions were measured during the test. In this paper, the effect of echo reduction of the acoustic baffle was discussed.