• 한국군사과학기술학회지
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• 제3권1호
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• pp.76-84
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• 2000

As first step of any acoustic defence system, a attacking target warning system needs to be extremely reliable. This means the system must ensure a high probability of target classification together with a very low false alarm rate. In this paper, a algorithms for underwater target automatic classification is available for use in the passive sonar will be presented. In first, we will describe the precise automatic extraction of frequency lines for the detection of acoustic signatures. Also, a neural network and fuzzy based algorithms for target classification will be described. Thus the performances of these algorithms are very good with a high probability of classification.

• 한국군사과학기술학회지
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• 제14권3호
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• pp.372-379
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• 2011

Estimating underwater target state variables(position, velocity, course, etc) is necessary to counteract threatening targets. In passive sonar systems, conventional target motion analysis(TMA) techniques using bearing and frequency measurements of an underwater target are widely introduced. However, it is not clear how conventional TMA techniques can be used if some of frequency measurements are unavailable during parts of the scenario, partially unavailable frequency measurements are common in the ocean with complicated acoustic conditions where frequency measurements often come and go. This paper proposes a new TMA algorithm, which is robust to partially unavailable frequency measurements, using the frequency measurements fusion method.

• 한국해양공학회지
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• 제34권5호
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• pp.371-376
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• 2020

Underwater acoustics, which is the study of the phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. Underwater acoustics is mainly applied in the field of remote sensing, wherein information on a target object is acquired indirectly from acoustic data. Presently, machine learning, which has recently been applied successfully in a variety of research fields, is being utilized extensively in remote sensing to obtain and extract information. In the earlier parts of this work, we examined the research trends involving the machine learning techniques and theories that are mainly used in underwater acoustics, as well as their applications in active/passive SONAR systems (Yang et al., 2020a; Yang et al., 2020b; Yang et al., 2020c). As a follow-up, this paper reviews machine learning applications for the inversion of ocean parameters such as sound speed profiles and sediment geoacoustic parameters.

• 로봇학회논문지
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• 제11권3호
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• pp.146-155
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• 2016

Localization of underwater vehicle is essential to use underwater robotic systems for various applications effectively. For this purpose, this paper presents a method of two-dimensional SLAM for underwater vehicles equipped with two hydrophones. The proposed method uses directional angles for underwater acoustic sources. A target signal transmitted from acoustic source is extracted using band-pass filters. Then, directional angles are estimated based on Bayesian process with generalized cross-correlation. The acquired angles are used as measurements for EKF-SLAM to estimate both vehicle location and locations of acoustic sources. Through these processes, the proposed method provides reliable estimation for two dimensional locations of underwater vehicles. Experimental results demonstrate the performance of the proposed method in a real sea environment.

• 한국멀티미디어학회논문지
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• 제18권11호
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• pp.1261-1267
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• 2015

The problem of underwater target detection and classification has been attracted a substantial amount of attention and studied from many researchers for both military and non-military purposes. The difficulty is complicate due to various environmental conditions. In this paper, we study classifier ensemble methods for active sonar target classification to improve the classification performance. In general, classifier ensemble method is useful for classifiers whose variances relatively large such as decision trees and neural networks. Bagging, Random selection samples, Random subspace and Rotation forest are selected as classifier ensemble methods. Using the four ensemble methods based on 31 neural network classifiers, the classification tests were carried out and performances were compared.

• 한국군사과학기술학회지
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• 제5권1호
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• pp.37-44
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• 2002

An echo signal in the active sonar using a high frequency is mainly formed of a specular reflection from the surface of an object along with several equivalent scatter inside, which are characterized by the spatial distribution of the highlights on the object. This thesis proposed a model in which the synthesized echo signal can be expressed as a distributed simulated target. The proposed model is obtained after composing a signal based on the movement of highlights relative to the aspect angle from the discontinuous point of an external hull with a strong reflection from a spheroid underwater target. Because the proposed algorithm includes a synthesis of the signals related to the highlight spacial distribution, it can be applied to all kinds of systems used at a short range, and similar results were obtained to the actual measured results of all reflected signals in previous literature referring to the irregular factor application of an envelope.

• 한국정보통신학회논문지
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• 제13권12호
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• pp.2753-2758
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• 2009

해군에서는 수중 표적을 효과적으로 탐색하기 위한 소나운용전술을 개발하기 위해 노력하고 있다. 효율적인 소나운용전술 개발을 위해서는 먼저 다양한 소나운영전술에 대한 효과도를 분석할 수 있는 시뮬레이션 시스템이 필요하다. 시뮬레이션 시스템은 해양환경 정보, 자함 정보, 소나 정보, 그리고 수중표적의 정보를 매개변수로 입력받아 소나운용전술에 대한 시뮬레이션을 수행하며, 시뮬레이션의 진행에 따른 다양한 정보를 제공할 수 있어야 한다. 본 연구에서는 다양한 환경에서 수중표적에 대한 함정의 최적 탐색 전략을 평가할 수 있는 탐색효과도 분석용 시뮬레이션 시스템을 설계 개발하였다. 시뮬레이션 시스템은 소나방정식 및 탐지확률곡선을 이용할 수 있도록 개발되었으며, 표적의 실제적인 행동패턴을 고려하여 여러 가지 형태의 기동 패턴을 시스템에 묘사하였다. 개발된 시스템은 앞으로 수중표적에 대한 효율적인 소나운용전술을 개발하고 발전시키는데 유용하게 사용될 수 있을 것으로 판단된다.

• 한국자기학회지
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• 제21권5호
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• pp.167-173
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• 2011

함정에 의한 수중 정자기장 신호는 수중 방사소음 신호에 비해 상대적으로 거리에 따라 급격히 감소되는 특성을 가지지만 근거리에서 정확한 표적 탐지가 가능하므로 감응 기뢰 체계에서 기폭 신호를 제공하는 신호원으로 널리 사용되고 있다. 본 논문에서는 상용 전자기 해석 도구를 활용하여 함정 선체, 내부 구조물 및 주요 탑재장비들에 의한 수중 정자기장 신호 특성 예측 결과에 대해 상세히 기술하였으며, 추가로 대상 함정에 소자코일을 배치하여 소자 효과도 분석을 수행하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.881-882
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• 2008

This paper presents an algorithm of target detection of an underwater acoustic signal by estimating the background. At first, subtract the estimated background from the underwater acoustic signal. To estimate the background, this paper uses an algorithm of Denoising. By using Thresholding and Power analysis, we extract targets from the signal to eliminate the background. The proposed method is valuable as an algorithm to reduce calculation amounts of multi frames we will apply.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.316-320
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• 2003

This paper introduces an autonomous underwater vehicle (AUV) model, ASUM, equipped with a visual servo control system to dock into an underwater station with a camera and motion sensors. To make a visual servoing AUV, this paper implemented the visual servo control system designed with an augmented state equation, which was composed of the optical flow model of a camera and the equation of the AUV's motion. The system design and the hardware configuration of ASUM are presented in this paper. ASUM recognizes the target position by processing the captured image for the lights, which are installed around the end of the cone-type entrance of the duct. Unfortunately, experiments are not yet conducted when we write this article. The authors will present the results for the AUV docking test.