• 한국군사과학기술학회지
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• 제25권1호
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• pp.30-44
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• 2022

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5527-5545
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• 2019

Through-wall ultra-wide band (UWB) radar has been considered as one of the preferred and non-contact technologies for the targets detection owing to the better time resolution and stronger penetration. The high time resolution is a result of a larger of bandwidth of the employed UWB pulses from the radar system, which is a useful tool to separate multiple targets in complex environment. The article emphasised on human subject localization and detection. Human subject usually can be detected via extracting the weak respiratory signals of human subjects remotely. Meanwhile, the range between the detection object and radar is also acquired from the 2D range-frequency matrix. However, it is a challenging task to extract human respiratory signals owing to the low signal to clutter ratio. To improve the feasibility of human respiratory signals detection, a new method is developed via analysing the standard deviation based kurtosis of the collected pulses, which are modulated by human respiratory movements in slow time. The range between radar and the detection target is estimated using joint time-frequency analysis (JTFA) of the analysed characteristics, which provides a novel preliminary signature for life detection. The breathing rates are obtained using the proposed accumulation method in time and frequency domain, respectively. The proposed method is validated and proved numerically and experimentally.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1064-1066
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• 2003

In this paper, ground penetrating radar (GPR), which has the capability to detect non metal and plastic mines, is proposed to detect and discriminate antipersonnel (AP) landmines. The time domain GPR - Impulse radar and frequency domain GPR - SFCW (Stepped Frequency Continuous Wave) radar is utilized for metal and non-metal landmine detection and its performance is investigated. Since signal processing is vital for target reorganization and clutter rejection, we implemented the MUSIC (Multiple Signal Classification) algorithm for the signal processing of SFCW radar data and SAR (Synthetic Aperture Radar) processing method for the signal processing of Impulse radar data.

• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.424-433
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• 2008

An ownship with towed array sonar (TAS) has limited maneuvers due to its dynamic feature, bearing and frequency measurements of a target which are not detected continuously but are often lost in ocean environment. We propose a pre-processing algorithm for the faded bearing and frequency measurements to solve the BFTMA problem of TAS under limited detection conditions. The proposed pre-processing algorithm to restore the faded bearing and frequency measurements is implemented to perform a BFTMA filter even if the measurements of a target are not continuously detected. The Modified Gain Extended Kalman Filter (MGEKF) method based on the Interacting Multiple Model (IMM) structure is applied for a BFTMA filter algorithm to estimate the target. Simulations for the various conditions were carried out to verify the applicability of the proposed algorithms, and confirmed superior estimation performance compared with the existing Bearings-Only TMA (BOTMA).

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 C
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• pp.1322-1323
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• 2006

This research aims to develop the multiple channel electrochemical DNA chip that has the above characteristic and be able to solve the problems. At first, we fabricated a high integration type DNA chip array by lithography technology. It is able to detect a plural genes electrochemically after immobilization of a plural probe DNA and hybridization of non-labeling target DNA on the electrodes simultaneously.

• 한국음향학회지
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• 제35권2호
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• pp.110-117
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• 2016

수동소나 시스템에서는 함정의 소음원에서 발생하는 방사 소음을 분석하여 표적을 탐지 및 식별한다. 소나의 탐지 범위 안에 다수의 소음원이 존재하면 신호를 분석할 때 각 소음원에서 나오는 성분들이 혼합되어 각각의 소음원을 규명하기가 어렵다. 이를 해결하기 위해 일반적으로는 배열 센서를 이용한 빔을 형성하여 소음원의 신호를 공간적으로 분리하는 기법이 사용되지만 환경에 따라 여전히 어려운 점이 있다. 본 연구에서는 수동소나 표적신호를 분리하기 위한 새로운 방법으로 주파수영역 독립성분분석(FDICA: Frequency Domain Independent Component Analysis)을 적용하고, 혼합된 표적신호를 분리하는 모의실험을 수행하여 그 타당성을 검증하였다. 표적신호 합성을 위한 특징 정보로는 기계류 토널 성분 및 프로펠러 성분을 사용하였고, 분리 전 후의 결과를 LOFAR(Low Frequency Analysis and Recording), DEMON(Detection Envelope Modulation On Noise) 분석을 통해 비교하였다.

• 한국전자파학회논문지
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• 제30권3호
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• pp.252-259
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• 2019

제트 엔진 변조(jet engine modulation: JEM) 신호는 제트 엔진 고유 정보인 날개 수를 제공하기 때문에 HRRP(High-Range Resolution Profile), ISAR(Inverse Synthetic Aperture Radar)와 함께 표적 인식 분야에서 널리 이용된다. 제트 엔진의 날개 수를 얻기 위해서는 날개 수에 비례하는 chopping 주파수를 추출하는 것이 중요하다. 기존의 chopping 주파수 추출 방법은 초기 문턱값(threshold)를 정의하고, 이를 줄여가며, chopping peak를 탐지하는 방법을 사용한다. 하지만 이러한 탐지 방법은 반복적 찾음에 따라 신호에 따라 시간이 많이 소요되는 단점을 가지고 있다. 이에 본 논문에서는 MUSIC(MUltiple SIgnal Classification) 알고리즘을 이용하여 chopping 주파수를 추출하는 것을 제안한다. 주어진 JEM 신호에 MUSIC 알고리즘을 적용하여 chopping 주파수를 찾아 날개 수 후보군을 정하고, 후보군들의 점수를 위한 다른 chopping 주파수 추출에도 MUSIC 알고리즘을 적용하도록 한다. 반복적으로 찾는 기존의 탐지 알고리즘과 달리, 한번에 찾아내기 때문에 정확한 chopping 주파수를 찾을 뿐만 아니라, 계산 시간도 줄일 수 있음을 입증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5095-5111
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• 2019

A multi-rotor Unmanned Aerial Vehicle (UAV) system is developed to solve the manhole cover detection problem for the infrastructure maintenance in the suburbs of big city. The visible light sensor is employed to collect the ground image data and a series of image processing and machine learning methods are used to detect the manhole cover. First, the image enhancement technique is employed to improve the imaging effect of visible light camera. An imaging environment perception method is used to increase the computation robustness: the blind Image Quality Evaluation Metrics (IQEMs) are used to percept the imaging environment and select the images which have a high imaging definition for the following computation. Because of its excellent processing effect the adaptive Multiple Scale Retinex (MSR) is used to enhance the imaging quality. Second, the Single Shot multi-box Detector (SSD) method is utilized to identify the manhole cover for its stable processing effect. Third, the spatial coordinate of manhole cover is also estimated from the ground image. The practical applications have verified the outdoor environment adaptability of proposed algorithm and the target detection correctness of proposed system. The detection accuracy can reach 99% and the positioning accuracy is about 0.7 meters.

• Current Optics and Photonics
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• 제4권3호
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• pp.210-220
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• 2020

Aiming at the problem that the Local Sparse Difference Index algorithm has low accuracy and low efficiency when detecting target anomalies in a hyperspectral image, this paper proposes a Weighted Collaborative Representation and Sparse Difference-Based Hyperspectral Anomaly Detection algorithm, to improve detection accuracy for a hyperspectral image. First, the band subspace is divided according to the band correlation coefficient, which avoids the situation in which there are multiple solutions of the sparse coefficient vector caused by too many bands. Then, the appropriate double-window model is selected, and the background dictionary constructed and weighted according to Euclidean distance, which reduces the influence of mixing anomalous components of the background on the solution of the sparse coefficient vector. Finally, the sparse coefficient vector is solved by the collaborative representation method, and the sparse difference index is calculated to complete the anomaly detection. To prove the effectiveness, the proposed algorithm is compared with the RX, LRX, and LSD algorithms in simulating and analyzing two AVIRIS hyperspectral images. The results show that the proposed algorithm has higher accuracy and a lower false-alarm rate, and yields better results.

• 한국산학기술학회논문지
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• 제11권3호
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• pp.978-983
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• 2010

무선 센서 네트워크에서 음향 표적의 식별은 환경 감시, 침입 감시, 다중 표적 분리 등에서 많이 연구된다. 무선 센서 네트워크의 센서 노드에서 사용하는 기존의 신호 처리기법은 표적으로부터 수신된 신호의 에너지를 계산하여 표적의 존재 유무만을 기지국으로 전송하는 방법과 수신 신호를 압축하여 전송하는 방법이 많이 사용되었다. 전자의 경우 표적의 감시를 위한 무선 센서 네트워크에서는 표적의 정보가 한정적이므로 적합하지 않고 후자의 경우는 센서 노드에서의 신호처리 및 전송에 소모되는 에너지가 높아 센서의 생존시간이 줄어들게 된다. 따라서 본 논문에서는 표적의 감시를 위한 무선 센서 네트워크에서 필요한 시간정보와 표적의 주파수 정보를 포함하는 센서 노드에서의 특징 추출 기법을 제안한다. 본 논문에서는 웨이블릿 변환을 이용하여 추출된 웨이블릿 상수에서 표적의 시간 정보와 잡음이 제거된 표적의 식별 정보를 추출함으로서 센서 노드에서 에너지 효율적인 신호처리를 구현하고 추출된 특징을 전송하여 통신에 소모되는 에너지를 원신호 대비 28%로 줄이는 알고리듬을 제안한다.