• 한국정보통신학회논문지
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• 제23권9호
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• pp.1123-1130
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• 2019

항공기용 레이다 시스템의 효율적인 개발 및 성능 시험을 위해서는 탑재된 레이다 시스템의 특성 때문에 발생하는 강력한 클러터들의 모의 구현이 필수적이다. 이러한 클러터들의 모의 구현이 효율적으로 이루어질 수 있다면 실험실내에서의 알고리즘 검증, 분석 및 성능 평가 등이 가능하다. 따라서 매우 경제적이면서도 효과적인 시스템 설계 및 구현이 이루어질 수 있다. 그러나 이러한 클러터들은 항공기의 비행경로 또는 안테나 빔의 조향 각도, 지표면 반사정도 등에 따라 매우 다양한 형태로 발생하게 되고 따라서 클러터의 모의 구현이 매우 까다롭고 번거롭다는 것이 가장 큰 문제점이다. 따라서 본 논문에서는 항공기용 레이다에서 발생하는 다양한 형태의 클러터들을 효율적으로 모의구현할 수 있는 범용 도플러 전력 스펙트럼 모델을 제안하였다. 또한 이러한 범용 스펙트럼 모델에서의 파라미터 값들의 변경 및 조정을 통하여 실제로 시스템에서 필요로 하는 시간영역에서의 다양한 클러터들을 가변적으로 용이하게 발생시킬 수 있음을 보였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2088-2091
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• 2003

We presents a new algorithm for tracking person in video sequence that integrates the meanshift iteration procedure into the particle filtering. Utilizing the nice property of convergence to the modes in the meanshift iteration we show that only a few sample points are sufficient, while in general the particle filtering requires a large number of sample points. Multi-parts of a person is tracked independently of each other based on the color Then, the similarity against the reference model color and the geometric constraints between multi-parts are reflected as the sample weights. Also presented is the computer simulation results, which show successful tracking even for complex background clutter.

• 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.

• 전기학회논문지
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• 제66권8호
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• pp.1242-1249
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• 2017

In this paper, we propose a new algorithm for the performance analysis of the sidelobe blanker (SLB) in radar system, which is based on the matrix pencil method (MPM). In general, the SLB in radar is composed of the main antenna, the auxiliary antenna, and the processing unit. The auxiliary antenna with wide beamwidth receives interference signals such as jamming or clutter signals. The main antenna with high gain receives the target signal in the main beam and the interference signals in the sidelobe. In this paper the Swerling model is used as the target echo signal by considering a probabilistic radar cross section (RCS) of the target. To estimate the SLB performance it needs to calculate the probability of target detection and the probability of blanking the interference by using the signals received from the main and auxiliary antennas. The detection probability and the blanking probability include multiple summations of infinite series with infinite integrations, of which convergence rate is very slow. Increase of summation range to improve the calculation accuracy may lead to an overflow error in computer simulations. In this paper, to resolve the above problems, we used the MPM to calculate a summation of infinite series and improved the calculation accuracy and the convergence rate.