• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.683-686
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• 1999

In this paper, we develop an active sonar signal synthesis model to analyze the detection performance of active sonar systems in a shallow water environment. Using this model, we synthesize the return signal of a bistatic sonar system at a typical operating frequency. This signal can be used to test proper active sonar signal processing techniques for real applications.

• 한국군사과학기술학회지
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• 제16권2호
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• pp.118-129
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• 2013

Since the obstacles and mines are of the risk factors for operating ships and submarines, the active sonar system is inevitably used to avoid the hazards in ocean environment. In this paper, modeling and simulation algorithm is used for active sonar systemto quantify the measure of mission achievability, which is known as Measure of Effectiveness(MOE), specifically for detection in this study. MOE for detection is directly formulated as a Cumulative Detection Probability(CDP) calculated from Probability of Detection(PD) in range and azimuth. The detection probability is calculated from Transmission Loss(TL) and the sonar parameters such asDirectivity Index (DI) calculated from the shape of transmitted and received array, steered beam patterns, and Reverberation Level (RL). The developed code is applied to demonstrating its applicability.

• 한국멀티미디어학회논문지
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• 제18권1호
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• pp.9-16
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• 2015

Various approaches to process active sonar signals are under study, but there are many problems to be considered. The sonar signals are distorted by the underwater environment, and the spatio-temporal and spectral characteristics of active sonar signals change in accordance with the aspect of the target even though they come from the same one. And it has difficulties in collecting actual underwater data. In this paper, we synthesized active target echoes based on ray tracing algorithm using target model having 3-dimensional highlight distribution. Then, Fractional Fourier transform was applied to synthesized target echoes to extract feature vector. Recognition experiment was performed using probabilistic neural network classifier.

• 한국해양공학회지
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• 제8권1호
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• pp.62-70
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• 1994

In order to estimate the detection range of a active SONAR system, the SONAR equation is commonly used. In this paper, an algorithm to calculate detection range in active SONAR system as function of SONAR depth and target depth is presented. For given SONAR parameters and environment, the transmission loss and background level are found, signal excess is computed. Using log-normal distribution, signal excess is converted to detection probability at each range. Then, the detection range is obtained by integrating the detection probability as function of range for each depth. The proposed algorithm have been applied to the case of omni-directional source with center frequency 30Hz for summer and winter sound profiles. It is found that the optimal search depth is the source depth since the detection range increase at source depth where the signal excess is maximized.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.172.5-172
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• 2001

Target Motion Analysis(TMA) for Passive Sonar Systems with bearing-only measurements needs to enhance system observability to improve target tracking performance by ownship maneuvering. However, tracking problem incurred by weak observaility result in slow convergence of the target estimates. On the other hand, active sonar systems do not have problem associated with system observaility. However, it drawback related to system survivability. In this paper, the algorithm that could be used in Active/passive Mixed-Mode Sonar Systems is proposed to analyze maneuvering target motion and to improve TMA performance. The proposed TMA algorithm is tested by a series of computer simulation runs and the results ...

• 한국멀티미디어학회논문지
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• 제20권10호
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• pp.1637-1645
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• 2017

Target/Nontarget classification can be divided into the study of shape estimation of the target analysing reflected echo signal and of type classification of the target using acoustical features. In active sonar system, the feature vectors are extracted from the signal reflected from the target, and an classification algorithm is applied to determine whether the received signal is a target or not. However, received sonar signals can be distorted in the underwater environments, and the spatio-temporal characteristics of active sonar signals change according to the aspect of the target. In addition, it is very difficult to collect real sea-trial data for research. In this paper, target/non-target classification were performed using real sea-trial data. Feature vectors are extracted using MFCC(Mel-Frequency Cepstral Coefficients), filterbank energy in the Fourier spectrum and wavelet domain. For the performance verification, classification experiments were performed using backpropagation neural network classifiers.

• 한국멀티미디어학회논문지
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• 제21권9호
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• pp.1062-1067
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• 2018

Conventional active sonar technology has relied heavily on the hearing of sonar operator, but recently, many techniques for automatic detection and classification have been studied. In this paper, we extract the image data from the spectrogram of the active sonar signal and classify the extracted data using CNN(convolutional neural networks), which has recently presented excellent performance improvement in the field of pattern recognition. First, we divided entire data set into eight classes depending on the ratio containing the target. Then, experiments were conducted to classify the eight classes data using proposed CNN structure, and the results were analyzed.

• 한국음향학회지
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• 제34권1호
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• pp.75-81
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• 2015

능동소나를 이용한 대잠전 환경에서 클러터는 표적탐지 및 추적성능을 저하시키는 가장 큰 원인 중 하나이다. 본 논문에서는 중주파수 능동소나에서 표적 피크 신호의 통계적 특징을 이용한 단일 핑 클러터 제거 기법을 제안한다. 기존의 표적 피크 영역을 제외한 잔향 존재 영역에서 오탐지율을 줄이는 기법이나 여러 핑을 누적하여 기동 패턴을 분석하여 표적과 클러터를 구분하는 기법들의 단점을 보완하기 위하여 단일 핑 데이터의 표적 피크 영역에서 통계적 특징 정보를 이용하여 클러터와 표적신호를 구분한다. 실제 표적을 이용한 해상실험에서 성능을 검증하였으며 기존 대비 클러터가 약 80 % 이상 제거되는 것을 확인하였다.

• 전자공학회논문지
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• 제51권8호
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• pp.11-19
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• 2014

최근 능동 소나 시스템은 다중 목표물 탐지가 가능하도록 높은 거리 분해능을 얻기 위해 협대역 시스템에서 광대역 시스템으로 발전하고 있다. 하지만, 목표물 탐지 및 파라미터 추정 등의 성능 향상을 위해서는 광대역 신호처리가 요구되며, 이로 인해 연산량의 증가가 불가피하다. 본 논문에서는 hyperbolic frequency modulation (HFM) 펄스를 사용하는 광대역 능동소나 시스템에서 연산량의 증가를 최소화 하면서도 고속으로 목표물의 탐지 및 속도정보 추정을 할 수 있도록, 확장 레플리카를 이용한 광대역 HFM 탐지기 설계 방법을 제안한다. 모의실험을 통해 제안된 방법이 기존의 필터뱅크를 이용한 광대역 소나 탐지기법에 비해 탐지 및 도플러 추정에서 약간의 성능 열화가 있지만, 연산량 측면에서 매우 우수함을 보인다.

• 한국해양공학회지
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• 제34권4호
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• pp.277-284
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• 2020

Underwater acoustics, which is the study of 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. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.