• The Journal of the Acoustical Society of Korea
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• v.15 no.6
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• pp.104-109
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• 1996

In passive sonar system, frequency lines generated by underwater target are very important for detection, tracking and classification. In this paper, the extraction method of unstable frequency line from the time samples of the radiated noise of underwater target is studied. As unstable frequency line is time varying, an extended Kalman filter algorithm which is desirable for nonlinear system is applied to extract unstable frequency line. The proposed method shows good extraction of unstable frequency line by application of simulated signal and real target.

• The Journal of the Acoustical Society of Korea
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• v.19 no.8
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• pp.78-83
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• 2000

Passive sonar system is designed to classify the underwater targets by analyzing and comparing the various acoustic characteristics such as signal strength, bandwidth, number of tonals and relationship of tonals from the extracted tonals and frequency lines. First of all the precise detection and extraction of signal frequency lines is of particular importance for enhancing the reliability of target classification. But, the narrowband frequency lines which are the line formed in spectrogram by a tonal of constant frequency in each frame can be detected weakly or discontinuously because of the variation of signal strength and transmission loss in the sea. Also, it is very difficult to detect and extract precisely the signal frequency lines by the complexity of impulsive ambient noise and signal components. In this paper, the automatic detection and extraction method that can detect and extract the signal components of frequency tines precisely are proposed. The proposed method can be applied under the bad conditions with weak signal strength and high ambient noise. It is confirmed by the simulation using real underwater target data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.546-555
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• 2013

In this paper, we propose a novel method to enhance the harmonic components from the frequency lines of the passive sonar signals. For this, we first separate the stable frequency lines from unstable ones using mean and difference of spectral bins in the vernier analysis. Then we emphasize the harmonic components using autocorrelation-based postprocessing, and enhance them by reducing the background noise with the split-window two pass mean algorithm. Experimental results for real underwater acoustic data are presented with our discussions.

• The Journal of the Acoustical Society of Korea
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• v.15 no.5
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• pp.118-124
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• 1996

Signal frequency lines generated from the acoustic targets are of particular importance for target detection and classification in passive sonar systems. The underwater noise consists of a mixture of ambient noise and radiated noise of targets. Detction of exact signal frequency lines depends on signal detection threshold and variation of ambient noise. In this paper, a detection method of signal frequency lines for acoustic targets using autoassociative memory (ASM) neural network, which is not sensitive to variation of signal detection threshold and ambient noise, is proposed. It is confirmed by simulation and application of real acoustic targets that the proposed method shows good performance for detection of signal frequency lines.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.181-184
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• 2000

수중신호의 스펙트로그램상에 형성되는 신호 주파수선은 토널의 신호 세기와 바다 자체의 전달 특성 등으로 인하여 미약하게 탐지되거나 불규칙하게 끊어져서 불연속하게 되며 또한 임펄스성의 주변잡음 성분과 혼재하여 어느 토널이 연속적으로 탐지되는지가 모호하게 되는 경우가 많고 정밀하게 신호 성분만을 탐지, 추출하기가 어렵다. 따라서 본 논문에서는 신호 세기가 미약한 경우나 높은 주변잡음이 복합되어 있는 경우에도 정밀하게 신호 성분만을 탐 지, 추출할 수 있는 협대역 다중 주파수선의 자동 탐지 및 추출을 위한 기법을 제안한다. 제안된 알고리즘에 실제 수중표적 신호를 적용하여 제안된 알고리즘이 매우 유용함을 보인다.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.78-88
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• 2024

Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.2
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• pp.389-396
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• 1999

In this paper, we propose a multiple underwater object classification and tracking algorithm using the narrowband tonal and frequency line features extracted from the frequency spectrum of the acoustic signal. The general algorithm using the wideband and narrowband energy has a high tracking error when objects are close and cross each other. But the proposed algorithm shows a good tracking performance for the simulation scenarios generated by the real acoustic data.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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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.

• Proceedings of the Optical Society of Korea Conference
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• 1995.06a
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• pp.30-34
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• 1995

13C2H2분자의 1.54949$\mu$m 회전-진동 전이 흡스선을 이용하여 DFB LD의 발전주파수를 안정화 시킴으로써 이를 주파수대욕 광 다중통신(OFDM)의 표준주파수를 광동기 신호로 활용하는 방안에 대하여 연구하였다. 본 연구에서는 DFB LD의 발진주파수의 변화에 따른 기체 Cell의 투과한 빛의 세기의 변화를 연산 처리하여 이를 안정화 Loop에 대한 error 신호로 사용하여 줌으로써 MHz 이내에 주파수 안정도를 갖는 광 동기신호를 구성하였다. 이러한 안정화 방법을 이용하여 이제까지 제안된 다른 방법에 비하여 광학계통과 광신호처리 과정을 크게 단순화 된, 또한 동작중 자체 진단 및 자동복구 기능을 갖춘 이상적인 신호를 구성하였다.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.1
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• pp.51-58
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• 1997

In passive sonar, the frequency spectrum of a sound radiated by underwater moving targets is composed of a broadband nonuniform background noise and narrowband discrete tonals. To detect the tonals, the background noise is estimated and removed. Using the existing algorithms that estimate the background noise, a week tonals are not detected. Because a freuqency line that is formed by tonals which are being extracted continuously is a feture of the target, we are nessesory to efficiently detect the tonals that compose the frequncy line. In this paper, we propose an efficient neural network that can remove automatically the background and detect the even errl tonals, and we extract the frequency line feature on the spectrogram by the proposed algorithm. The experimental results for a ship's radiated sound show a better performance in comparison with the existing TPM algorithm.