• International Journal of Aeronautical and Space Sciences
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• 제10권1호
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• pp.83-90
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• 2009

A new IRF (Impulse Response Function) analysis technique in high resolution SAR image is presented by taking into account the real clutter environment. In order to investigate the realistic effect of clutter background on the impulse response function of SAR image, an ideally generated impulse response function is superimposed with a large number of background clutter data which are extracted from the various regions of an actual SAR image. As a performance measure, PSLR (Peak Sidelobe Ratio) of the clutter-contained IRF is presented in the various groups of clutter background, and finally the results are compared with the stochastic model.

• 대한원격탐사학회지
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• 제24권2호
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• pp.99-106
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• 2008

영상 레이다(SAR)는 주야간, 일조량에 관계없이 전천후로 영상획득이 가능하여 군사용으로는 물론 과학 민수용으로 광범위하게 활용된다. SAR 시스템에서는 고도, 운용 주파수, PRF 등의 다양한 시스템설계 파라미터로부터 생성된 임펄스 응답 함수(impulse response function)를 분석하여 공간해상도, PSLR, ISLR 등 영상품질 성능 파라미터의 추정이 가능하다. 그러나 모델링된 임펄스 응답 특성은 주변 클러터 환경이 고러되지 않은 이상적인 경우이므로 실제 주변 클러터 환경을 고려한 SAR 영상품질 분석 기법이 필요하다. 본 논문에서는 먼저 주요 SAR시스템 파라미터를 기반으로 SAR 점표적 원시 데이터를 생성하고, 거리-도플러 알고리듬(range-Doppler algorithm)을 이용하여 임펄스 응답 데이터를 형성한다. 그리고 실제 SAR영상의 일부분을 추출하여 주변 배경 클러터 환경 데이터를 형성한 후, 임펄스 응답 데이터를 삽입한다. 형성된 응답 데이터는 영상품질의 정확도를 향상시키고자 확장보간법을 도입하여 분석하고, 이에 대한 효과를 주요 도플러 파라미터인 방위 FM율 오차에 따른 성능분석을 수행함으로써 확인한다.

• 한국군사과학기술학회지
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• 제17권2호
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• pp.175-181
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• 2014

In this paper, we propose a robust clutter pre-thresholding method using background adaptive Threshold Map for the clutter rejection in the complex coastal environment. The proposed algorithm is composed of the use of Threshold Map's and method of its calculation. Additionally we also suggest an automatic decision method of Thresold Map's update. Experimental results on some sets of real infrared image sequence show that the proposed method could remove clutters effectively without any loss of detection rate for the aim target and reduce processing time dramatically.

• 로봇학회논문지
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• 제16권1호
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• pp.49-55
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• 2021

Recently, a smart farm technology is drawing attention as an alternative to the decline of farm labor population problems due to the aging society. Especially, there is an increasing demand for automatic harvesting system that can be commercialized in the market. Pre-harvest crop detection is the most important issue for the harvesting robot system in a real-world environment. In this paper, we proposed a real-time tomato instance tracking algorithm by using deep learning and probability models. In general, It is hard to keep track of the same tomato instance between successive frames, because the tomato growing environment is disturbed by the change of lighting condition and a background clutter without a stochastic approach. Therefore, this work suggests that individual tomato object detection for each frame is conducted by YOLOv3 model, and the continuous instance tracking between frames is performed by Kalman filter and probability model. We have verified the performance of the proposed method, an experiment was shown a good result in real-world test data.

• 한국군사과학기술학회지
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• 제14권4호
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• pp.676-683
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• 2011

In this paper, we propose a robust method which can detect and track a high-speed small approaching target in a cluttered environment for Korean Active Protection System. The proposed method uses a temporal and spatial filter, tracking filter to detect and track a single target in consecutive order. And it is comprised of a candidate target detection step, a prior target selection step and a target tracking. Field tests on real infrared image sequences show that the proposed method could stably track a high speed and small target in complex background and target occlusion.