• Journal of Space Technology and Applications
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• v.1 no.3
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• pp.337-355
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• 2021

This paper is made to suggest a future strategy for the Korean High Resolution Wide Swath Synthetic Aperture Radar (HRWS SAR) satellite development by surveying the current trends for the SAR satellite technologies. From the survey, the latest SAR technology trends are revealed of using Digital Beam-Forming (DBF), SCan-On-Receive (SCORE), Displaced Phase Center Antenna (DPCA), interferometry, and polarimetry for exploiting the SAR imagery. Based on the latest SAR technology trends and the foreign HRWS SAR development cases, the strategy for the future HRWS Korean SAR satellite development is suggested to develop the DPCA and SCORE technologies by using the KOrea Multi-Purpose SATellite-6 (KOMPSAT-6) which is going to launch in a few years, and consequently to develop the HRWS SAR satellites which can monitor the whole Earth at weekly intervals.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.2
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• pp.139-146
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• 2017

A ground moving target's velocity estimation algorithm applicable for a SAR-GMTI system using 2 channel displaced phase center antenna(DPCA) is proposed. In this algorithm, we assume target's across-track velocity can be estimated by along-track interferometry (ATI) and present a method to estimate target's along-track velocity. To accomplish this method, we first transform a radar-target geometry in which a moving target has zero velocity via altering a radar velocity such that target's velocity is reflected into it and next manipulate the spectral centers of the subapertures within the synthetic aperture. The validity of the proposed algorithm is demonstrated through simulation results showing the performance of the target's velocity estimation and the enhancement of reconstructed target image quality in terms of resolution and SINR.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.06a
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• pp.343-345
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• 2014

실시간 위치 기반 선박 충돌 위험도는 자선의 관점에서 선박충돌의 위험을 판단하는 것이 아니라 VTS(Vessel Traffic Service)의 관점에서 충돌 위험이 있는 선박을 식별하고 충돌 위험 지역을 전자 해도에서 실시간으로 확인하여 해당 해역 전체의 선박 교통흐름과 통항하는 선박간의 위험도를 평가하는 것이 목적이다. 항해사로써의 승선 경험과 관제사로써의 근무 경험, 그리고 다 년간 VTS 관제 업무를 수행하고 있는 관제사들로부터 충돌의 위험이 있는 선박을 식별하는 방법으로 주로 선박간의 벡터(코스와 속력)를 실시간으로 모니터링하여 충돌 위험이 있는 선박에게 피항 조치를 취하도록 정보를 제공하는 것으로 확인되었다. 따라서 DCPA(Distance to Closest Point of Approach)와 TCPA(Time to Closest Point of Approach), 그리고 최근접시간을 변수로 하는 충돌 위험 함수식(최대값=100)을 연구하여 각 지점의 위험도를 실시간으로 표시하는 기초 모델을 연구하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.10a
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• pp.63-65
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• 2013

실시간 위치 기반 선박 충돌 위험도는 자선의 관점에서 선박충돌의 위험을 판단하는 것이 아니라 VTS(Vessel Traffic Service)의 관점에서 충돌 위험이 있는 선박을 식별하고 충돌 위험 지역을 전자 해도에서 실시간으로 확인하여 해당 해역 전체의 선박 교통흐름과 통항하는 선박간의 위험도를 평가하는 것이 목적이다. 항해사로써의 승선 경험과 관제사로써의 근무 경험, 그리고 다 년간 VTS 관제 업무를 수행하고 있는 관제사들로부터 충돌의 위험이 있는 선박을 식별하는 방법으로 주로 선박간의 벡터(코스와 속력)를 실시간으로 모니터링하여 충돌 위험이 있는 선박에게 피항 조치를 취하도록 정보를 제공하는 것으로 확인되었다. 따라서 DCPA(Distance to Closest Point of Approach)와 TCPA(Time to Closest Point of Approach), 그리고 최근접시간을 변수로 하는 충돌 위험 함수식(최대값=100)을 연구하여 최대 위험값을 가지는 지점과 주변의 위험값을 계산하여 해역 전체의 위험도를 실시간으로 표시하는 기초 모델을 연구하였다.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.4
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• pp.17-27
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• 2007

The enhanced distributed channel access (EDCA) of IEEE 802.11e has been standardized for supporting Quality of Service (QoS) in wireless LANs. In the EDCA, support of QoS can be achieved statistically by reducing the probability of medium access for lower priority traffics. In other words, it provides statistical channel access rather than deterministically prioritized access to high priority traffic. Therefore, lower priority traffics affect the performance of higher priority traffics. Consequently, at the high loads, the EDCA does not guarantee the QoS of multimedia applications such as voice and video even though it provides higher priority. In this paper, we propose a simple and effective scheme, called deterministic priority channel access (DPCA), for improving the QoS performance of the EDCA mechanism. In order to provide guaranteed priority channel access to multimedia applications, the proposed scheme uses a busy tone for limiting the transmissions of lower priority traffics when higher priority traffic has data packets to send. Performance of the proposed scheme is investigated by numerical analysis and simulation. Our results show that the proposed scheme outperforms the EDCA in terms of throughput, delay, jitter, and drop under a wide range of contention levels.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.103-108
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• 2022

In this paper, we propose a new image analysis method based on regression manifold 3-D PCA. The proposed method is a new image analysis method consisting of a regression analysis algorithm with a structure designed based on an autoencoder capable of nonlinear expansion of manifold 3-D PCA and PCA for efficient dimension reduction when entering large-capacity image data. With the configuration of an autoencoder, a regression manifold 3-DPCA, which derives the best hyperplane through three-dimensional rotation of image pixel values, and a Bayesian rule structure similar to a deep learning structure, are applied. Experiments are performed to verify performance. The image is improved by utilizing the fine dust image, and accuracy performance evaluation is performed through the classification model. As a result, it can be confirmed that it is effective for deep learning performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.423-430
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• 2013

In typical face recognition systems, the frontal view of face is preferred to reduce the complexity of the recognition. Thus individuals may be required to stare into the camera, or the camera should be located so that the frontal images are acquired easily. However these constraints severely restrict the adoption of face recognition to wide applications. To alleviate this problem, in this paper, we address the problem of tracking and recognizing faces in video captured with no environmental control. The face tracker extracts a sequence of the angle/size normalized face images using IVT (Incremental Visual Tracking) algorithm that is known to be robust to changes in appearance. Since no constraints have been imposed between the face direction and the video camera, there will be various poses in face images. Thus the pose is identified using a PCA (Principal Component Analysis)-based pose classifier, and only the pose-matched face images are used to identify person against the pre-built face DB with 5-poses. For face recognition, PCA, (2D)PCA, and $(2D)^2PCA$ algorithms have been tested to compute the recognition rate and the execution time.