• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.203-208
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• 2010

In this paper, we convert underwater acoustic signal made by sonar system into digital image. We propose the algorithm that detects target candidate and emphasizes information of target introducing image processing technique for the digital image. The process detecting underwater target estimates background noise in underwater acoustic signal changing irregularly, recomposes it. and eliminates background from original image. Therefore, it generates initial target group. Also, it generates weighted map through proceeding doppler information, ensures information for target candidate through filtering using weighted map for image eliminated background noise, and decides the target candidate area in the single frame. In this paper, we verified that proposed algorithm almost had eliminated the noise generated irregularly in underwater acoustic signal made by simulation, targets had been displayed more surely in the image of underwater acoustic signal through filtering and process of target detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4A
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• pp.316-323
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• 2002

In this paper, we propose a counter-countermeasure (CCM) in a crossed array tracker for efficient target tracking under countermeasures (CM) operated by a target. The proposed CCM adepts two detection bands and uses the signal distribution characteristics to discriminate between the target and the CMs. To avoid wrong detection of a target position due to missing signal pulses, it predicts current target position based on previous target position. To evaluate the performance of the proposed CCM in a crossed array tracker, we perform numerical simulations for target signal extraction and target tracking under various conditions. The simulation results show that the proposed CCM removes the CM effect well and tracks the target efficiently.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.5 no.1
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• pp.100-110
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• 1994

레이다 신호는 대표적인 전자파 신호로서 주변환경에 따라 시간, 주파수, 공간 영역에서 고유한 신호특성을 가지고 있으며, 신호처리 기법도 다양하다. 본 논문에서는 먼저 레이다를 위한 전파 신호처리 의정의와 필요성을 언급한뒤, 레이다 신호환경 특성을 살펴보고 신호처리를 위한 신호의 시간 및 스펙트럼 특성에 대해 기술하였다. 그리고, 신호특성에 적합한 신호처리기의 구현을 위해 레이다 신호처리에 관 련된 주요 기법에 대해 개괄적으로 설명하였다. 레이다 신호처리 분야는 일반적으로 잘 알려진 음성이 나 영상신호처리 분야와 달리 고유한 알고리듬과 구조가 요구된다. 신호처리기법으로서 레이다 파형설 계, 해상도 모호성, 펄스압축, 클러터제거, 도플러처리, 일정오경보탐지, 클러터 지도, 표적군 형성/ 추출, 표적식별, 레이다영상기법, 적응배열처리 등에 관해 개괄적으로 설명하였다. 레이다 선호처리 기술은 "스마트"한 레이다를 위한 두뇌 역할을 하기때문에 그 필요성과 중요성이 증가하고 있다. 그러나, 고속, 대용량의 신호를 주어진 빔 주사시간동안에 실시간으로 처리하여 표적 정보를 추출해야 하기 때문에 아직도 상용 프로세서의 속도 한계내에서 알고리듬의 수행에 다소 제약을 받고 있으나, 최근 디지탈 신호처리 전용의 고속 칩의 출현으로 많은 발전을 가져오고 있다. 끝으로, 향후 레이다 신호처리 발전 추세와 응용분야에 대해 살펴보았다. 응용분야는 군수 및 민수용의 겸용 파급효과가 매우 크고, 군용의 대공탐색 및 조기경보, 전장감시뿐만 아니라 전투기 탑재용으로 필수적이며, 특히 민수용의 공 항, 항공기, 선박, 위성 등 매우 다양하다. 최근 발전추세에 따른 기술로서 다중모드 신호처리, 고집적 회로기술, 적응배열, 디지탈 빔형성, 적응성, 고분해능 및 방향성, 표적식별, 다차원 신호처리에 대해 언급 하였다.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1
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• pp.34-39
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• 1996

Target state estimation is a fundamental problem of the sonar signal processing. In this paper, the covariance analysis techniques are applied to enhance the performance of the target state estimation of a monopulse sonar system. MOST, the artificial target signal generator based on the highlight model is used to generate signals in various target states. The performance of the developed method has been evaluated by applying it to the various S/N. The enhanced performance of the covariance analysis method presented in this paper is discussed.

• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.8-15
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• 2016

As a generalized form of the conventional Fourier transform, fractional Fourier transform (FrFT) can analyze a signal at intermediate domain between time and frequency domains with a transform order ${\alpha}$. Especially, FrFT has a number of advantages in the analysis of LFM (Linear Frequency Modulation) signals due to its robustness to noise. In this paper, we have proposed a new method to detect and estimate the distance of the target from the FrFT spectrum of the received echo signal. Experimental results have validated the proposed method, and shown that reliable target distance could be estimated in noise and reverberation environments.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.2
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• pp.116-122
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• 2002

A timing signal board which can be used to analyze EA effect for coherent radar systems is introduced. It is capable of generating the timing signals that are needed for EA test about radar systems in real time. Its function to generate baseband target signal makes it easy to analyze EA effect. Because all parameters of timing signals can be changed by software, it is very easy to configure many kinds of test scenarios.

• Journal of the Korea Institute of Military Science and Technology
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• v.4 no.1
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• pp.93-103
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• 2001

A reflection signal in an active sonar using a high frequency is mainly formed of a specular reflection from the surface of an object along with several equivalent scatters inside, which are characterized by the spatial distribution of the highlight on the object. This study analyze the existing echo signal synthesis models eq, random distribution model, equivalent interval distribution model & MUTAHID(Modified Underwater TArget by HIlight Distribution) model for simulated target, and compare the characteristics of the reflected signal synthesis results for each model in various conditions. These highlight distribution models can be efficiently applied to the simulated target signals synthesis of various real systems requiring the echo signal synthesis on the underwater target.

• Journal of IKEEE
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• v.22 no.4
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• pp.1044-1049
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• 2018

CNN (Convolutional Neural Networks) is a neural network that models animal visual information processing. And it shows good performance in various fields. In this paper, we use CNN to classify target and non-target data by analyzing the spectrogram of active sonar signal. The data were divided into 8 classes according to the ratios containing the targets 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 seven classes corresponding to the target signal sequentially appear only at the position of the target signal.

• Journal of IKEEE
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• v.24 no.4
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• pp.1034-1038
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• 2020

In order to effectively process target recognition using radar, accurate signal information for the target is required. However, such a target signal is usually mixed with noise, and this part of the study is continuously carried out. Especially, image processing, target signal processing and target recognition for the target are examples. Since the field of target recognition is important from a military point of view, this paper carried out research on target recognition of aircraft using a tree-structured fuzzy neural networks. Fuzzy neural networks are learned by using reflected signal data for an aircraft to optimize the model, and then test data for the target are used for the optimized model to perform an experiment on target recognition. The effectiveness of the proposed method is verified by the simulation results.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.953-956
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• 2009

무선 센서 네트워크에서 이동하는 표적의 위치를 추정하기 위한 연구는 센서 노드의 에너지가 제한 되어 있어 센서 노드 상호간의 협력적인 신호처리 기법이 필수적이다. 기존의 무선 센서 네트워크에서 이동 표적의 위치를 추정하는 방법은 각 센서 노드에서 이동 표적으로부터 수신된 신호를 이용해 수신신호강도, 잡음제거, 압축 등의 신호처리를 수행하고 기지국으로 전송하는 형태이다. 이런 기존의 기법은 애드 호크 방법의 무선 센서 네트워크에는 적용이 어렵고, 각 센서 노드에서 신호처리 및 통신에 의한 에너지 소모가 크기 때문에 무선 센서 네트워크의 생존 시간이 짧아지게 된다. 본 논문에서는 웨이블릿 기반의 무선 센서 네트워크의 협력적 신호 처리 방법을 제안한다. 제안된 방법은 웨이블릿 변환을 이용한 센서노드에서의 에너지 효율적 특징추출을 수행하고 각 센서 노드간의 특징 전송을 통해 표적의 위치를 추정한다.