• Journal of Advanced Navigation Technology
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• v.10 no.4
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• pp.377-384
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• 2006

In this paper the detection time of the distress signal for the satellite-based search and rescue (SAR) system is evaluated. Present LEOSAR system in operation employs a few Low-altitude Earth Orbit (LEO) satellites and hence provides poor and local coverage availability. This results in a considerably long waiting time for a distress beacon to be detected by a rescue mission control center. One can expect that the detection time of the distress signal will be significantly reduced if the proposed MEOSAR system, which is based on the Medium-altitude Earth Orbit (MEO) satellites, is implemented. Taking into account the influence of the obstacles on the beacon signal, simulations are carried out to evaluate the detection time of distress signals for the LEOSAR and MEOSAR systems and the corresponding results are analyzed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4503-4508
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• 2010

A direction finding(DF) technology of a signal is very important for electronic warfare and has studied for a long time. The method of TDOA(time difference of arrival) is one of good DF methods in this time, and that is to receive an emitter signal with two antennas, to measure the time difference of a signal at two antennas, and converse the time difference to direction of the signal. For small DF error, high time resolution receiver and long baseline are needed. In this paper we suggest a good baseline with adaptive antenna arrangement into 10m*10m area.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3
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• pp.149-153
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• 2008

General underwater target detection methods adopt short-time FFT for estimate target doppler. This paper proposes the efficient target detection method, instead of conventional FFT, using approximate FFT for distributed sensor network target detection, which requires lighter computations. In the proposed method, we decrease computational rate of FFT by the quantization of received signal. For validation of the proposed method, experiment result which is applied to FFT based active sonar detector and real oceanic data is presented.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.599-602
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• 2016

고속철도 산업의 핵심 요소 중 하나인 선로전환기는 열차의 진로를 제어해주는 부품으로, 해당 설비의 노후화를 조기에 탐지하여 적절한 시기에 선로전환기를 교체하는 것은 안정적인 철도운영에서 매우 중요하다. 본 논문에서는 선로전환기의 작동 시 발생하는 전류 신호를 이용하여 선로전환기의 노후화를 탐지하는 시스템을 제안한다. 제안하는 시스템은 선로전환기로부터 전류 신호를 취득한 후, 주파수 도메인의 특징인 SK값으로 변환하여 특징벡터를 추출하고, PCA를 이용하여 SK벡터의 차원 축소와 동시에 중요한 특징들만을 선택한다. 마지막으로, 선로전환기의 노후화를 탐지하는 문제를 이진 클래스 문제로 해석하여, 기계학습의 대표적 모델인 SVM을 이용하여 선로전환기의 노후화 여부를 탐지한다. 실제 국내에서 운행 중인 선로전환기의 전류 신호를 취득하여 실험한 결과, 선로전환기의 노후화 상황을 안정적으로 탐지함을 확인하였다.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.233-247
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• 2023

Recently, research on detecting objects in hidden spaces beyond the direct line-of-sight of observers has received attention. Most studies use optical equipment that utilizes the directional of light, but sound that has both diffraction and directional is also suitable for non-line-of-sight(NLOS) research. In this paper, we propose a novel method of detecting objects in non-line-of-sight (NLOS) areas using acoustic signals in the audible frequency range. We developed a deep learning model that extracts information from the NLOS area by inputting only acoustic signals and predicts the properties and location of hidden objects. Additionally, for the training and evaluation of the deep learning model, we collected data by varying the signal transmission and reception location for a total of 11 objects. We show that the deep learning model demonstrates outstanding performance in detecting objects in the NLOS area using acoustic signals. We observed that the performance decreases as the distance between the signal collection location and the reflecting wall, and the performance improves through the combination of signals collected from multiple locations. Finally, we propose the optimal conditions for detecting objects in the NLOS area using acoustic signals.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.467-470
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• 2004

잠수함 정숙화 추세와 복잡한 해양 환경으로 대잠수함전에서 미약한 표적신호를 지속적으로 탐지하기 매우 어려워지고 있어 소나 운용자가 장시간 지속적으로 전방위 표적 탐색하는 부담이 매우 크므로 표적 자동탐지 추적 기능이 필수적이다. 본 논문에서는 장거리 예인 수동소나에 적합한 표적의 자동 탐지 및 추적기법을 제안하고 시뮬레이션과 실제 해상 환경에서 수중 표적신호로 성능을 검증하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.98-105
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• 2003

We propose a new technique of deinterleaving multiple radar pulse sequences by means of genetic algorithm for threat identification in electronic warfare(EW) system. The conventional approaches based on histogram or continuous wavelet transform are so deterministic that they are subject to failing in detection of individual signal characteristics under real EW signal environment that suffers frequent signal missing, noise, and counter-EW signal. The proposed algorithm utilizes the probabilistic optimization procedure of genetic algorithm. This method, a time-of-arrival(TOA) only strategy, constructs an initial chromosome set using the difference of TOA. To evaluate the fitness of each gene, the defined pulse phase is considered. Since it is rare to meet with a single radar at a moment in EW field of combat, multiple solutions are to be derived in the final stage. Therefore it is designed to terminate genetic process at the prematured generation followed by a chromosome grouping. Experimental results for simulated and real radar signals show the improved performance in estimating both the number of radar and the pulse repetition interval.

• Journal of KSNVE
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• v.8 no.4
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• pp.749-756
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• 1998

기어에서 충격성 진동 및 소음은 치차의 이상과 연관이 있다. 따라서 충격 진동 및 소리는 기어의 이상 진단에 사용되어 질 수 있다. 또한 이들 충격파를 조기에 정확하게 탐지하여 기어의 이상을 진단하면 완전 파손을 방지할 수 있다. 그러나 주변 소음 및 노이즈 신호 때문에 객관적이 충격파의 탐지가 어렵기 때문에, 본 논문은 이러한 숨겨진 충격 신호를 능동 신호 처리 기법을 이용하여 조기에 찾아내고 이것을 시간-주파수 영역에서 해석하였다.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3
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• pp.123-128
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• 2007

This Paper Presents new transient signal classification algorithms for underwater transient signals. In general. the ambient noise has small spectral deviation and energy variation. while a transient signal has large fluctuation. Hence to detect the transient signal, we use the spectral deviation and power variation. To classify the detected transient signal. the feature Parameters are obtained by using the Wigner-Ville distribution based eigenvalue decomposition. The correlation is then calculated between the feature vector of the detected signal and all the feature vectors of the reference templates frame-by-frame basis, and the detected transient signal is classified by the frame mapping rate among the class database.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.175-179
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• 2007

To existing method which uses energy variation and spectrum deviation to detect the underwater transient signal is useful to detect white noise environment, but it is not useful to do colored noise environment. To improve capacity of detecting the underwater transient signal both in white noise environment and colored noise environment, this study takes advantage of Double Density Discrete Wavelet Transform and CFAR Power-Law.