능동소나 스펙트로그램 이미지와 CNN을 사용한표적/비표적 식별
Target/non-target classification using active sonar spectrogram image and CNN
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초록
CNN(Convolutional Neural Networks)은 동물의 시각정보처리과정을 모델링한 신경망으로 다양한 분야에서 좋은 성능을보여주고 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.
Abstract
CNN (Convolutional Neural Networks) is a neural network that models animal visual information processing. And itshows good performance in various fields. In this paper, we use CNN to classify target and non-target data by analyzingthe spectrogram of active sonar signal. The data were divided into 8 classes according to the ratios containing thetargets and used for learning CNN. The spectrogram of the signal is divided into frames and used as inputs. As a result,it was possible to classify the target and non-target using the characteristic that the classification results of the sevenclasses corresponding to the target signal sequentially appear only at the position of the target signal.
참고문헌 (12)
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