• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.365-380
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• 2022

The target detection algorithm based on supervised learning is the current mainstream algorithm for target detection. A high-quality dataset is the prerequisite for the target detection algorithm to obtain good detection performance. The larger the number and quality of the dataset, the stronger the generalization ability of the model, that is, the dataset determines the upper limit of the model learning. The convolutional neural network optimizes the network parameters in a strong supervision method. The error is calculated by comparing the predicted frame with the manually labeled real frame, and then the error is passed into the network for continuous optimization. Strongly supervised learning mainly relies on a large number of images as models for continuous learning, so the number and quality of images directly affect the results of learning. This paper proposes a dataset STAR-24K (meaning a dataset for Space TArget Recognition with more than 24,000 images) for detecting common targets in space. Since there is currently no publicly available dataset for space target detection, we extracted some pictures from a series of channels such as pictures and videos released by the official websites of NASA (National Aeronautics and Space Administration) and ESA (The European Space Agency) and expanded them to 24,451 pictures. We evaluate popular object detection algorithms to build a benchmark. Our STAR-24K dataset is publicly available at https://github.com/Zzz-zcy/STAR-24K.

• International Journal of Aeronautical and Space Sciences
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• 제9권2호
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• pp.114-120
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• 2008

In the vision-based recovery phase, a terminal guidance for the blended-wing UAV requires visual information of high accuracy. This paper presents the light source target design and detection algorithm for vision-based UAV recovery. We propose a recovery target design with red and green LEDs. This frame provides the relative position between the target and the UAV. The target detection algorithm includes HSV-based segmentation, morphology, and blob processing. These techniques are employed to give efficient detection results in day and night net recovery operations. The performance of the proposed target design and detection algorithm are evaluated through ground-based experiments.

• International Journal of Aeronautical and Space Sciences
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• 제6권1호
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• pp.8-16
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• 2005

This paper describes target detection performance of millimeter wave radar that exits on non-stationary target detection schemes in the ground clutter conditions. The comparison of various CFAR process schemes such as CA(Cell-Average)-CFAR, GO(Greatest Of)/SO(Smallest Of)-CFAR, and OS(Order Statistics)-CFAR performance are applied. Using matlab software, we show the performance and loss between target detection probability and signal to noise ratio. This paper concludes the OS-CFAR process performance is better than any others and satisfies the optimal detection probability without loss of detection in the homogeneous clutter, When range bins increase.

• 한국군사과학기술학회지
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• 제11권1호
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• pp.66-74
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• 2008

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection. Scale invariant feature using the Laplacian scale-space can detect different sizes of targets robustly compared to the conventional spatial filtering methods with fixed kernel size. Additionally, scale-reflected adaptive thresholding can reduce many false alarms. Experimental results with real IR images show the robustness of the proposed target detection in real world.

• 한국전자파학회논문지
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• 제28권8호
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• pp.661-669
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• 2017

본 논문에서는 잠망경을 탐지할 수 있는 항공용 레이다를 이용한 MMTI(Maritime Moving Target Indicator)에 대해 기술한다. 먼저 해상 클러터와 해상 표적의 특성을 알아보고, GMTI(Ground Moving Target Indicator)와 MMTI의 차이를 분석하여, 최적의 MMTI 운용환경 및 운용방법을 제안한다. 그리고 저속의 작은 해상 표적을 탐지하기 위하여 STAP(Space-Time Adaptive Processing)을 활용한 신호처리 알고리즘을 제시한다. 시뮬레이션을 통해 다양한 RCS에 대한 2채널 시스템과 3채널 시스템의 최소탐지속도 탐지확률을 분석하고, 거리 정확도, 속도 정확도, 방위각 정확도와 같은 주요 성능 변수를 분석한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권3호
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• pp.1173-1192
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• 2015

The Support Vector Data Description (SVDD) has achieved great success in anomaly detection, directly finding the optimal ball with a minimal radius and center, which contains most of the target data. The SVDD has some limited classification capability, because the hyper-sphere, even in feature space, can express only a limited region of the target class. This paper presents an anomaly detection algorithm for mitigating the limitations of the conventional SVDD by finding the minimum volume enclosing ellipsoid in the feature space. To evaluate the performance of the proposed approach, we tested it with intrusion detection applications. Experimental results show the prominence of the proposed approach for anomaly detection compared with the standard SVDD.

• 대한전자공학회논문지SP
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• 제49권4호
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• pp.120-128
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• 2012

본 논문에서는 불균일한 클러터 환경에서 다중 표적탐지 성능을 향상시키기 위한 변형된 반복 백색화 투영 통계(modified iterative pre-whitening projection statistics: MIPPS) 기법을 제안하였다. MIPPS 기법은 항공기용 레이더에서 사용하는 시공간 적응 처리(space-time adaptive processing) 알고리듬의 불균일성 검출(non-homogeneity detection: NHD) 기법으로 반사신호 세기가 서로 다른 다수의 표적이 근접거리에 혼재되어 있는 환경에서 우수한 표적탐지 성능을 나타낸다. 모의실험을 통해 기존의 다양한 NHD 기법들의 성능을 분석하고, 본 논문에서 제안하는 MIPPS 기법이 강한 표적신호에 의해 야기되는 마스킹 효과(masking effect)를 최소화하면서 반사신호 세기가 약한 표적에 대한 평균 탐지 확률을 향상시킬 수 있음을 확인하였다.

• 한국항공우주학회지
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• 제39권9호
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• pp.848-857
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• 2011

영상센서 기반의 충돌회피 시스템을 구성하기 위해서는 수 픽셀 이내의 낮은 신호대잡음비 환경에서 다수의 표적을 탐지할 수 있는 알고리듬이 필요하다. 이처럼 영상 내에서 희미하게 나타나는 잠재적인 표적과 잡음을 구분하기 위한 방법으로서 연속적인 영상 정보를 효율적으로 처리하는 Track-Before-Detect (TBD) 알고리듬이 연구되고 있다. 본 논문에서는 기존의 TBD 알고리듬을 확장하여 다수 표적 탐지 요구조건을 만족시키기 위한 두 가지 방식의 기법을 제시하였다. 첫 번째 방식은 동적 계획법과 K-평균 클러스터링 기법에 기반을 두고 있으며 두 번째 방식은 은닉 마르코프 모델에 Sub-Window 기법을 적용하였다. 제안한 방식의 성능 및 차이점은 수치해석 결과를 통해 분석하였다.

• Current Optics and Photonics
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• 제6권4호
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• pp.420-429
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• 2022

In this paper, the details and design process of an optical system for space target detection cameras are introduced. The whole system is divided into three structures. The first structure is a short-focus visible light system for rough detection in a large field of view. The field of view is 2°, the effective focal length is 1,125 mm, and the F-number is 3.83. The second structure is a telephoto visible light system for precise detection in a small field of view. The field of view is 1°, the effective focal length is 2,300 mm, and the F-number is 7.67. The third structure is an infrared light detection system. The field of view is 2°, the effective focal length is 390 mm, and the F-number is 1.3. The visible long-focus narrow field of view and visible short-focus wide field of view are switched through a turning mirror. Design results show that the modulation transfer functions of the three structures of the system are close to the diffraction limit. It can further be seen that the short-focus wide-field-of-view distortion is controlled within 0.1%, the long-focus narrow-field-of-view distortion within 0.5%, and the infrared subsystem distortion within 0.2%. The imaging effect is good and the purpose of the design is achieved.

• 한국항공우주학회지
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• 제30권2호
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• pp.75-82
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• 2002

불확실한 측정값 근원의 문제에서는 표적을 최적으로 탐지해내는 것이 유용하다. 본 논문에서는 클러터 환경에서 표적을 추적하는 경우에 탐지확률 및 오경보확률과 동시에 탐지문턱값 처리에 따른 추적오차를 살펴보고, 문턱값과 표적추적 유효화영역의 최적화 알고리즘을 제안한다. 제안한 알고리즘은 시뮬레이션을 통해 상태추정오차공분산의 측면에서 성능을 분석한다.