• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.203-208
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• 2010

In this paper, we convert underwater acoustic signal made by sonar system into digital image. We propose the algorithm that detects target candidate and emphasizes information of target introducing image processing technique for the digital image. The process detecting underwater target estimates background noise in underwater acoustic signal changing irregularly, recomposes it. and eliminates background from original image. Therefore, it generates initial target group. Also, it generates weighted map through proceeding doppler information, ensures information for target candidate through filtering using weighted map for image eliminated background noise, and decides the target candidate area in the single frame. In this paper, we verified that proposed algorithm almost had eliminated the noise generated irregularly in underwater acoustic signal made by simulation, targets had been displayed more surely in the image of underwater acoustic signal through filtering and process of target detection.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.234-242
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• 2024

We propose a passive sonar signal classification algorithm using Graph Neural Network (GNN). The proposed algorithm segments spectrograms into image patches and represents graphs through connections between adjacent image patches. Subsequently, Graph Convolutional Network (GCN) is trained using the represented graphs to classify signals. In experiments with publicly available underwater acoustic data, the proposed algorithm represents the line frequency features of spectrograms in graph form, achieving an impressive classification accuracy of 92.50 %. This result demonstrates a 8.15 % higher classification accuracy compared to conventional Convolutional Neural Network (CNN).

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.164-173
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• 2014

In underwater robotics, vision would be a key element for recognition in underwater environments. However, due to turbidity an underwater optical camera is rarely available. An underwater imaging sonar, as an alternative, delivers low quality sonar images which are not stable and accurate enough to find out natural objects by image processing. For this, artificial landmarks based on the characteristics of ultrasonic waves and their recognition method by a shape matrix transformation were proposed and were proven in Part 1. But, this is not working properly in undulating and dynamically noisy sea-bottom. To solve this, we propose a framework providing a selection phase of likelihood candidates, a selection phase for final candidates, recognition phase and tracking phase in sequence images, where a particle filter based selection mechanism to eliminate fake candidates and a mean shift based tracking algorithm are also proposed. All 4 steps are running in parallel and real-time processing. The proposed framework is flexible to add and to modify internal algorithms. A pool test and sea trial are carried out to prove the performance, and detail analysis of experimental results are done. Information is obtained from tracking phase such as relative distance, bearing will be expected to be used for control and navigation of underwater robots.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.3
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• pp.275-281
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• 2011

To acquire an underwater topographic information is necessary for the design and construction of structures in marine and inland water. It consists of water depth information by bathymetric survey and underwater bottom topography image information can be obtained by side scan sonar in different ways. For the purpose of providing high quality data by means of engineering site survey, it is necessary to apply simultaneous acquisition of two information and carry out the integrated interpretation to each other. The present research aims to obtain information of the underwater topography and water depth at the same time using interferometer technique, and to validate interferometer technique with accuracy estimation.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.121-129
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• 2021

This paper deals with a super-resolution that improves the resolution of side scan sonar images using learning-based compressive sensing. Learning-based compressive sensing combined with deep learning and compressive sensing takes a structure of a feed-forward network and parameters are set automatically through learning. In particular, we propose a method that can effectively extract additional information required in the super-resolution process through various initialization methods. Representative experimental results show that the proposed method provides improved performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) than conventional methods.

• 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.

• Electrical & Electronic Materials
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• v.9 no.8
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• pp.825-830
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• 1996

In this study, Characteristics of Ultrasonic Transducer fabricated with porous piezoelectric resonator, 3-D underwater object restoration using the self made ultrasonic transducer and modified SCL(Simple Competitive Learning) neural network are investigated. The self-made transducer was satisfied the required condition of ultrasonic transducer in water, and the modified SCL neural network using the acquired object data 16*16 low resolution image was used for object restoration of $32{\times}32$ high resolution image. The experimental results have shown that the ultrasonic transducer fabricated with porous piezoelectric resonator could be applied for SONAR system.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.2
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• pp.225-234
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• 2018

A Multibeam Echo Sounder (MBES) is commonly used for rapid seafloor mapping. We herein present a time-domain integrated system simulation technique for MBES development. The Modeling and Simulation (M&S) modules consist of four parts: sensor array signal transmission, propagation and backscattering modeling in the ocean environment, beamforming of the received signals, and image processing. Also, the simulation employs a ray-theory-based algorithm to correct the reconstructed bathymetry, which has errors due to the refraction caused by the vertical sound velocity profile. The developed M&S technique enables design parameter verification and system parameter optimization for MBES. The framework of this technique can also be potentially used to characterize the seabed properties. Finally, typical seafloor images are presented and discussed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1081-1087
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• 2015

Recently, the researches of aerial drones are actively executed in various areas, the researches of surface drones and underwater drones are also executed in marine areas. In case of surface drones, they essentially utilize acoustic information by the sonar and consequently have the local information in the obstacle avoidance as the sonar has the limitations due to the beam width and detection range. In order to overcome this, more global method that utilizes optical images by the camera is required. Related to this, the aerial drone with the camera is desirable as the obstacle detection of the surface drone with the camera is impossible in case of the existence of clutters. However, the dynamic-floating aerial drone is not desirable for the long-term operation as its power consumption is high. To solve this problem, a collaborative obstacle avoidance method based on the acoustic information by the sonar of the surface drone and the optical image by the camera of the static-floating aerial drone is proposed. To verify the performance of the proposed method, the collaborative obstacle avoidances of a MSD(Micro Surface Drone) with an OAS(Obstacle Avoidance Sonar) and a BMAD(Balloon-based Micro Aerial Drone) with a camera are executed. The test results show the possibility of real applications and the need for additional studies.

• The Journal of the Acoustical Society of Korea
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• v.40 no.6
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• pp.546-554
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• 2021

In the signal processing of synthetic aperture sonar, it is subject that the platform in which the sensor array is installed moves along the straight line path. In practical operation in underwater, however, the sensor platform will have trajectory disturbances, diverting from the line path. It causes phase errors in measured signals and then produces deteriorated SAS images. In this study, in order to develop towed SAS, as tools to remove the phase errors associated with the trajectory disturbances of the towfish, motion compensation technique using Redundant Phase Center (RPC) and also Phase Gradient Autofocus (PGA) method is investigated. The performances of these two approaches are examined by means of a simulation for SAS system having a sway disturbance.