• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.1077-1085
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• 2020

In order to Mine Countermeasure (MCM), the search is carried out for the expected mine zone. At this time, mine hunting uses Autonomous Unmanned Vehicle(AUV), taking into account the danger of mine and the stability of our forces. Sonar system for identifying buried mines are equipped with Side Scan Sonar(SSS) or Synthetic Aperture Sonar(SAS). This paper describes the analysis of mine hunting effects according to the commercial SSS characteristics. Based on the characteristics of each SSS, the insonified area and recognition probability were modeled, and the analysis was performed according to the search pattern of the AUV. AUV's search pattern defines three patterns depending on the presence or absence of SSS or shaded areas. The analysis results derived search time and detection probability for each search pattern, and finally, the improvement of search depending on the presence or absence of side injection or shaded area.

• Electronics and Telecommunications Trends
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• v.28 no.6
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• pp.167-179
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• 2013

측면주사음탐기(side scan sonar) 센서는 해저면의 영상을 실시간으로 탐색하는 장비로서 해양탐사 및 지질조사, 해저통신 및 어초조사, 기뢰 및 잠수정 탐색 등 해양탐사와 관련한 대표적 장비라고 할 수 있다. 센서는 해저와 목표물을 표시하기 위해 소나 플랫폼의 움직임을 사용하며, 동작주파수 범위는 20kHz~500kHz이다. 이 주파수는 요구되는 깊이와 목표물의 크기에 의해서 결정된다. 센서는 수직으로 $45^{\circ}$, 수평으로 $2^{\circ}$ 정도의 신호전파 방사각도 폭을 가진다. 최근에는 해양탐사와 개발을 위해 빠른 스캔속도와 정확한 정보, 고해상도의 영상을 얻기 위해 해저면에 대한 다중빔 영상센서의 핵심기술로 활용되면서 그 활용성과 중요성이 점차 증가되고 있다. 본고에서는 측면주사소나 센서의 기본 원리 및 종류, 디중빔측면주사소나 기술동향, 응용분야의 사례를 소개함으로써, 국내 기반기술 및 상용화 개발이 취약한 측면주사 음탐기 센서에 대한 이해를 돕고자 한다.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.73-81
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• 2018

The quality of side scan sonar images is determined by the frequency of a sonar. A side scan sonar with a low frequency creates low-quality images. One of the factors that lead to low quality is a high-level noise. The noise is occurred by the underwater environment such as equipment noise, signal interference and so on. In addition, in order to compensate for the transmission loss of sonar signals, the received signal is recovered by TVG (Time-Varied Gain), and consequently the side scan sonar images contain non-homogeneous noise which is opposite to optic images whose noise is assumed as homogeneous noise. In this paper, the SSCS (Structural Sparsity based Compressive Sensing) is proposed for removing non-homogeneous noise. The algorithm incorporates both local and non-local models in a structural feature domain so that it guarantees the sparsity and enhances the property of non-local self-similarity. Moreover, the non-local model is corrected in consideration of non-homogeneity of noises. Various experimental results show that the proposed algorithm is superior to existing method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2001.11b
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• pp.143-146
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• 2001

HDTV(high-definition television)와 개인용 컴퓨터의 발전에 따라 비월주사(interlaced scanning) 방식의 신호와 순차주사(progressive scanning) 방식의 신호 상호간의 변환에 대한 요구가 점점 늘어나고 있다. 이에 따라 비월주사 방식을 순차주사 방식으로 바꾸어주는 순차주사화(deinterlacing)가 활발히 연구되고 있다. 본 논문에서는 부화소(sub-pixel) 단위의 움직임 정보를 고려한 영상재결합 방법을 기반으로 한 순차주사화 알고리즘을 제안한다. 그러나 국부 움직임이 있는 영역에서는 영상재결합이 효과적이지 않으므로 적절한 문턱값을 이용하여 이러한 영역을 결정하고 에지를 이용한 보간 방법으로 순차주사화 한다. 또한 잘못된 움직임 추정으로 야기되는 문제들을 해결하기 위해, 각 영역과 입력 영상에 따라 다른 움직임 에러를 고려해서 정규화 값(regularization parameter)을 결정함으로 움직임 에러를 효율적으로 제거하였다 제안된 방법은 주관적 측면과 객관적인 측면에서 모두 우수한 결과를 실험적으로 보였다.

• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.246-254
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• 2020

In this paper, we propose a learning based compressive sensing algorithm for the purpose of side scan sonar image denoising. The proposed method is based on Iterative Shrinkage and Thresholding Algorithm (ISTA) framework and incorporates a powerful strategy that reinforces the non-linearity of deep learning network for improved performance. The proposed method consists of three essential modules. The first module consists of a non-linear transform for input and initialization while the second module contains the ISTA block that maps the input features to sparse space and performs inverse transform. The third module is to transform from non-linear feature space to pixel space. Superiority in noise removal and memory efficiency of the proposed method is verified through various experiments.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.12-20
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• 2018

Efforts have been made to reconstruct low-resolution underwater images to high-resolution ones by using the image SR (Super-Resolution) method, all to improve efficiency when acquiring side-scan sonar images. As side-scan sonar images are similar with the optical images with respect to exploiting 2-dimensional signals, conventional image restoration methods for optical images can be considered as a solution. One of the most typical super-resolution methods for optical image is a sparse coding and there are studies for verifying applicability of sparse coding method for underwater images by analyzing sparsity of underwater images. Sparse coding is a method that obtains recovered signal from input signal by linear combination of dictionary and sparse coefficients. However, it requires huge computational load to accurately estimate sparse coefficients. In this study, a sparse coding based underwater image super-resolution method is applied while a selective reconstruction method for object region is suggested to reduce the processing time. For this method, this paper proposes an edge detection and object and non object region classification method for underwater images and combine it with sparse coding based image super-resolution method. Effectiveness of the proposed method is verified by reducing the processing time for image reconstruction over 32 % while preserving same level of PSNR (Peak Signal-to-Noise Ratio) compared with conventional method.

• 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.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.1
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• pp.40-46
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• 2018

In order to successfully detect and identify underwater targets located on the seabed, unmanned surface vehicles (USVs) typically acquire acoustic signals with a side-scan sonar device and reconstruct information about the target from the processed images. As the quality of the side-scan sonar images acquired by USVs depends on the environment and operating parameters, using modeling and simulation techniques to design side-scan sonar devices can help optimize the reconstruction of the sonar images. In this work, we study a side-scan sonar design for use in USVs, that takes the movement of the platform into account. First, we constructed a simulated seabed environment with underwater targets, and specified the maneuvering conditions and sonar systems. We then generated the acoustic signals from the simulated environment using the sonar equation. Finally, we successfully imaged the simulated seabed environment using simple signal processing. Our results can be used to derive USV side-scan sonar design parameters, predict the resulting sonar images in various conditions, and as a basis for determining the optimal sonar parameters of the system.

• Journal of the Korean earth science society
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• v.34 no.1
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• pp.41-50
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• 2013

To analyze the distribution pattern of flood sediment deposits near the mouth of Chucheoncheon (river), side scan sonar images and seafloor sediment properties were investigated in the offshore area within about 50 m deep in water. Based on the analysis result of the sonar images, the seafloor of the study area is divided into three areas of basement, sandy-mud, and dispersed flood sediment. The colors of sonar images in each area are represented by dark black, light grey, and greyish black, respectively. The sediment composition in the grey black area shows 33.73% of gravel, 62.88% of sand, 3.37% of silt, and 0.02% of clay. On the other hand, the composition of the light grey area is 10.31% of sand, 56.42% of silt, and 33.27% of clay. Especially the sediment of the grey black area contains the considerable amount of burned plant fragments in black color, which could distinctly be differentiated from those in the offshore. The distribution pattern of the flood sediment deposits suggests that the land-originated detrital sediments seem to be transported from the Chucheon river into offshore along the shore rather than transversely. In conclusion, the longshore current of the study area is probably dominant to affect the spatial distribution of bottom features.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.382-390
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• 2021

Synthetic aperture sonars as well as side scan sonars or multibeam echo sounders have been commercialized and are widely used for seafloor imaging. In Korea related research such as the development of a towed synthetic aperture sonar system is underway. In order to obtain high-resolution synthetic aperture sonar images, it is necessary to accurately estimate the platform motion on which it is installed, and a precise underwater navigation system is required. In this paper we are going to provide reference data for determining the required navigation accuracy and precision of navigation sensors by quantitatively analyzing how much distortion of the sonar images occurs according to motion characteristics of the platform equipped with the synthetic aperture sonar. Five types of motions are considered and normalized root mean square error is defined for quantitative analysis. Simulation for error analysis with parameter variation of motion characteristics results in that yaw and sway motion causes the largest image distortion whereas the effect of pitch and heave motion is not significant.