• 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.

• 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.

• 한국정보통신학회논문지
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• 제17권5호
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• pp.1083-1088
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• 2013

수중 천해 환경에서 능동소나의 반향 신호로 기뢰를 탐지 및 식별하는 일은 복잡한 해양 환경의 영향으로 어려운 문제이다. SVM은 패턴인식 문제에서 최적의 해를 제공하는 이진 분류기이다. 본 논문에서는 SVM을 이용하여 능동소나의 반향 데이터로 기뢰와 같은 금속 물체와 바위를 식별하는 실험을 수행하면서, SVM에 사용되는 커널함수의 파라미터 값의 변화에 따른 식별 성능을 분석하고 제시하였다.

• 한국음향학회지
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• 제28권2호
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• pp.135-140
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• 2009

본 논문에서는 하이라이트 모델에 기반하여 능동소나의 표적신호를 합성하고, 합성된 신호를 이용하여 표적인식 실험을 수행하였다. 동일 표적이라도 표적의 자세각에 따라 다양한 형태의 파형을 갖는 신호가 합성되는데, 이에 대한 표적인식 결과를 알아보기 위해서 두 가지 방법으로 실험을 수행하였다. 하나는 고정된 여러 가지 자세각에 대한 표적신호에 대한 인식실험이고, 다른 하나는 임의의 자세각을 가지는 교신에 대만 인식 실험을 수행하였다. 인식실험을 위한 특징 인자로는 합성된 표적신호에 대해 시간영역에서 정합필터 및 포락선 검출을 통해 얻어지는 하이라이트 패턴을 사용하였으며, 패턴인식 기법으로는 다중클래스 SVM과 인공신경망을 사용하였다.

• 한국군사과학기술학회지
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• 제20권1호
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• pp.33-39
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• 2017

In this paper, an effective feature which is capable of classifying targets among the detections obtained from 2D range-bearing maps generated in active sonar environments is proposed. Most conventional approaches for target classification with the 2D maps have considered magnitude of peak and statistical features of the area surrounding the peak. To improve the classification performance, HOG(Histogram of Gradient) feature, which is popular for their robustness in the image textures analysis is applied. In order to classify the target signal, SVM(Support Vector Machine) method with reduced HOG feature by the PCA(Principal Component Analysis) algorithm is incorporated. The various simulations are conducted with the real clutter signal data and the synthesized target signal data. According to the simulated results, the proposed method considering HOG feature is claimed to be effective when classifying the active sonar target compared to the conventional methods.

• 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.

• 전기전자학회논문지
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• 제22권4호
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• pp.1044-1049
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• 2018

CNN(Convolutional Neural Networks)은 동물의 시각정보처리과정을 모델링한 신경망으로 다양한 분야에서 좋은 성능을 보여주고 있다. 본 논문에서는 CNN을 사용하여 능동소나 신호의 스펙트로그램을 분석하고, 표적과 비표적을 식별하는 연구를 수행하였다. 데이터를 표적이 포함된 비율에 따라 8클래스로 구분하고, CNN의 학습에 사용하였다. 신호의 스펙트로그램을 프레임별로 나누어 입력으로 사용한 결과, 표적신호의 위치에서만 표적신호에 해당하는 7개 클래스의 식별 결과가 순차적으로 나타나는 특성을 사용하여 표적과 비표적을 식별해낼 수 있었다.

• 한국정보통신학회논문지
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• 제16권10호
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• pp.2137-2142
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• 2012

수중환경 하에서 표적을 탐지하고 식별하는 문제는 군사적인 목적은 물론 비군사적 목적으로도 많은 연구가 수행되어 왔다. 수중환경에서의 수중음향 신호가 시간 공간적으로 특성이 변화하며 천해 다중경로 환경을 반영하는 복잡한 특성을 보이는 점으로 인해 능동 표적인식 기술은 매우 어려운 기술로 여겨져 왔다. 또한 실제 데이터 수집의 어려움이 따르게 된다. 본 논문에서는 수중환경 하에서 능동 표적신호를 합성, 특징추출 및 표적식별을 수행할 수 있는 시뮬레이터를 구현하였다. 표적신호의 합성에는 하이라이트 모델과 3차원 모델을 사용하였으며, 표적신호의 식별을 위해서는 다중각도에 기반한 은닉 마코프모델을 사용하였다.

• 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.