• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2792-2799
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• 2012

Multilateration that consists of three or more fixed nodes has been widely used in the field of indoor real time location system(RTLS). However, when one or two among fixed nodes are partially out of communication reachability due to obstruction and unstable node, it is difficult to obtain an efficient location information. In order to improve the challenges of poor ranging measurements and fluctuations in these environment, this paper presents, based on TOF(Time of Flight), a new algorithm which can reduce the inherent ranging measurements errors in the conventional multilateration using a vector prediction algorithm for the displacement estimation of mobile node and Kalman filter for an efficient distance average. Even if a person moves with mobile node and then it fails ranging measurement from some of fixed nodes, the proposed algorithm using a present and previous measurement value can predict the distance between mobile node and fixed node which fails ranging measurement. The experimental results are shown that the proposed system improves the localization accuracy and efficiency compared with conventional method, respectively.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06b
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• pp.446-450
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• 2010

다변측량법은 위치가 알려진 3개 이상의 지점으로부터 대상까지의 거리 정보를 바탕으로 대상의 위치를 계산하는 기법이다. Real Time Locating Systems (RTLS) 에서는 대상의 위치를 파악하기 위해 다변측량 기법을 주로 사용하는데, 이를 위해서는 리더와 대상과의 거리 정보가 3개 이상 필요하다. 그러나 항만 환경처럼 시설물 설치가 어렵고 장애물도 많은 넓은 지역에서는 음영지역이 다수 존재하여 이러한 조건을 항상 만족시키기 어렵다. 본 논문에서는 다변측량이 불가능한 적은 수의 거리 정보만으로 대상의 위치를 파악할 수 있도록 대상의 동선 정보를 이용하는 동선 정보 기반의 측위 기법을 제안한다. 성능 평가 결과 최소자승법만을 사용했을 때보다 평균 측위 오차가 약 3m, 표준 편차가 6m 줄었으며, 측위 성공률이 4.6 배 이상 높아졌다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.3
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• pp.162-168
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• 2017

Shallow-water bathymetry survey has been conducted using high definition color images obtained at the altitude of 100 m above sea level using a drone. Shallow-water bathymetry data are one of the most important input data for the research of beach erosion problems. Especially, accurate bathymetry data within closure depth are critically important, because most of the interesting phenomena occur in the surf zone. However, it is extremely difficult to obtain accurate bathymetry data due to wave-induced currents and breaking waves in this region. Therefore, optical remote sensing technique using a small drone is considered to be attractive alternative. This paper presents the potential utilization of image processing algorithms using multi-variable linear regression applied to red, green, blue and grey band images for estimating shallow water depth using a drone with HD camera. Optical remote sensing analysis conducted at Wolpo beach showed promising results. Estimated water depths within 5 m showed correlation coefficient of 0.99 and maximum error of 0.2 m compared with water depth surveyed through manual as well as ship-board echo-sounder measurements.