• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.06a
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• pp.287-289
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• 2014

선박의 선위 추측(DR, Dead Reckoning)은 수신되거나 측정된 위치 데이터와 속력, 침로 데이터만을 반영하여 계산하므로 대양의 항해나 연근해의 해역에서는 유효할 수 있으나 침로의 변경과 선속의 변화가 잦은 항계 내의 조선에 있어서는 적용이 어렵다는 문제점이 있다. 본 논문에서 제안하는 선박의 추측위치(DRP, Dead Reckoning Position)는 선박의 운항 패턴에 따라 항계 내의 항적 데이터를 수집하고, 수신된 위치 데이터와 속력, 침로 데이터를 점장위도항법(Mercator Sailing)을 통하여 계획항로(Planned Route)의 각 변침점(WP, Waypoint)간 침로(Course)와 항정(Ship's Passage)에 반영하였다. 제안된 추측위치 산출방법을 통하여 항계 내의 조선과정에서 계획항로를 선박의 추측위치와 결합하여 특정 시간 후의 상황패턴을 미리 예측하고 관제사의 의사결정에 기여하고자 한다.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.210-216
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• 2017

In the paper we propose and implement a new indoor localization system where the techniques of magnetic field based fingerprinting and pedestrian dead reckoning are combined. First, we determine a target's location by comparing acquired magnetic field values with a magnetic field map containing pre-collected field values at different locations and choosing the location having the closest value. As the target moves, we use pedestrian dead reckoning to estimate the expected moving path, reducing the maximum positioning error of the initial location. The system eliminates the problem of localization error accumulation in pedestrian dead reckoning with the help of the fingerprinting and does not require Wi-Fi AP infrastructure, enabling cost-effective localization solution.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.390-394
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• 2007

시간과 장소에 구애받지 않고 실시간으로 정보를 전달받을 수 있는 유비쿼터스 시대가 도래함에 있어서 실시간으로 움직이는 대상물의 위치를 알아내는 기술은 가장 근본적이며 필수적인 요소라 할 수 있다. 추측항법(Dead Reckoning)은 움직이는 대상물에 외부의 도움 없이 자신의 방향각과 가속도, 시간을 관측할 수 있는 관성항법장치(Inertial Navigation System)를 장착하여 이전의 위치 정보를 바탕으로 현재의 위치를 관측하는 방법이다. 또한 RFID(Radio Frequency IDentification)는 이러한 유비쿼터스 근거리무선통신의 핵심 기술로서 본 논문에서는 RFID에 기반한 위치 결정 시스템에 실시간 변화하는 대상물의 위치를 예측하기 위해 추측항법과 칼만필터(Kalman-filter)의 개념을 적용시켰다. 또한 RMSE(Root Mean Square Error)값을 통해 칼만필터의 적용에 따른 정확도의 향상과 각 디자인 요소들의 변화에 따라 위치의 정확도가 어떠한 변화를 갖는지를 분석하였다. 시뮬레이션 결과 칼만필터를 적용했을 때 이전보다 RMSE값이 현저히 작아지는 결과를 통해 위치의 정확도가 크게 향상되는 것을 확인하였다. 또한 RFID의 탐지 범위는 정확도에 큰 영향을 미칠 수 있는 주된 요소가 아니며, RFID 탐지 범위의 표준편차가 작을수록 위치 정확도는 높아지고, RFID 태그의 탐지 확률이 높을수록 RMSE 값의 변동이 작은 안정된 시스템을 갖으며 위치의 정확도 또한 높아진다는 것을 확인하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.1
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• pp.99-108
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• 2014

Recently, the interest in autonomous vehicle which is an aggregate of the automotive control technology is increasing. In particular, researches on the self-localization technology that is directly connected with stable driving of autonomous vehicle have been performed. Various dead reckoning technologies which are solutions for resolving the limitation of GPS have been introduced. However, the conventional dead reckoning technologies have two disadvantages to apply on the autonomous vehicle. First one is that the expensive sensors must be equipped additionally. The other one is that the accuracy of self-localization decreases caused by wheel slip when the vehicle's motion changed rapidly. Based on this background, in this paper, the wheel speed sensor which is equipped on most of vehicles was used and the dead reckoning algorithm considering wheel slip ratio was developed for autonomous vehicle. Finally, in order to evaluate the performance of developed algorithm, the various simulation were conducted and the results were compared with the conventional algorithm.

• Information and Communications Magazine
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• v.34 no.4
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• pp.17-24
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• 2017

본고에서는 저가형 관성 센서를 이용하여 실내 항법을 수행하는 여러 방법들에 대해 알아본다. 저가형 관성 센서를 이용한 추측 항법은 휴대성이 뛰어나고 외부의 인프라 없이 구현이 가능하고 가격이 저렴하다는 장점이 있지만, 오차가 빠르게 누적된다는 단점이 있다. 이를 해결하기 위해 사용자의 보행 특성을 이용한 보행자 추측 항법이 제안되었다. 본고에서는 보행자 추측 항법의 두 분류 기법인 걸음-이동방향 결합 기법과 관성 항법-영속도 보정 결합 기법의 원리와 각 기법들의 기술 동향에 대해 다루고자 한다.

• Annual Conference of KIPS
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• 2016.10a
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• pp.143-144
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• 2016

본 논문은 보행자의 각 발에 부착된 2개의 IMU(Inertial Measurement Unit) 정보를 융합하여 위치 추적 성능을 향상시키는 보행자 추측 항법 알고리즘을 제안하였다. 센서내의 방향드리프트로 인해 IMU기반 보행자 위치추적은 시간이 지남에 따라 성능이 크게 저하된다. 제안하는 알고리즘은 방향 드리프트로 인해 각 발의 이동경로가 발산하는 점에 착안하여, 보폭이 일정 값을 초과할 시 이를 보정하고 사용자의 위치를 계산한다. 실험을 통해 제안하는 알고리즘이 방향 드리프트를 효과적으로 감소시키는 것을 확인하였다.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.20 no.2
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• pp.105-111
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• 1984

Recently, Hybrid Navigation Systems combining Omega, NNSS, Loran C and Dead reckoning etc. served to give us highly accurate ship's position, and a number of ships are equipped with these navigation systems. In order to evaluate for the accuracy of this navigation system observations of Loran C, 5970 and 9970 chains and Radar at the same time were made on board m.s Jeonbuk 401 and 403 training ships of Gunsan Fisheries Collage at nine stations in the yellow sea from July, 1982 to June, 1983, and then were done by the Hybrid Navigation System combining Dead reckoning and Loran C at the same areas. The authors investigated the accuracy of the Hybrid Navigation System based on measurements of the relative positional error which is defined as the difference between the position fixed by this system or the Loran C system, and the one by the Radar. The obtained results are as follows; 1. The mean standard deviation of the time difference of Loran C were about 0.21$\mu$s in 9970 chain and about 0.06$\mu$s in 5970 chain, and the fluctuation of the time difference of Loran C in 5970 chain was smaller than that in 9970 chain. 2. The positional error between two positions by Radar and the Hybrid Navigation System in 9970 chain was about 0.4 miles, and between two positions by Radar and Loran C was about 0.51 miles. The Hybrid Navigation System was therefore more accurate than Loran C System. 3. The positional error between two positions by Radar and Hybrid Navigation System in 5970 chain was about 0.4 miles, and between two positions by Radar and computer simulation of Loran C was about 0.98 miles. Consequently, Hybrid Navigation System was more accurate than computer simulation of Loran C system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.6
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• pp.401-410
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• 2020

In this study, a real-time Tactical UAS position compensation system based on image information developed to compensate for the weakness of location navigation information during GPS signal interference and jamming / spoofing attack is described. The Tactical UAS (KUS-FT) is capable of automatic flight by switching the mode from GPS/INS integrated navigation to DR/AHRS when GPS signal is lost. However, in the case of location navigation, errors accumulate over time due to dead reckoning (DR) using airspeed and azimuth which causes problems such as UAS positioning and data link antenna tracking. To minimize the accumulation of position error, based on the target data of specific region through image sensor, we developed a system that calculates the position using the UAS attitude, EO/IR (Electric Optic/Infra-Red) azimuth and elevation and numerical map data and corrects the calculated position in real-time. In addition, function and performance of the image information based real-time UAS position compensation system has been verified by ground test using GPS simulator and flight test in DR mode.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.300-308
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• 2012

Many indoor location tracking technologies have been proposed. Generally indoor location tracking using TOA signal is used, there is a weak point that it's difficult to track the location due to obstacles like a refraction, reflection and dispersion of radio wave. In this paper, we apply ETOA(Estimated-TOA) algorithm in NLOS(Non-Line-Of-Sight) environment to solve above problem. In NLOS environment, TOA value between Beacon and Mobile node is predicted by ETOA algorithm and the tracking of indoor location is also possible to identify using two NLOS beacons of three beacons by this algorithm. We show that the proposed algorithm is accurate location tracking is accomplished using the applying the proposed algorithm to indoor moving robot and the inertia sensor of robot and Kalman filter algorithm.

• Journal of Advanced Navigation Technology
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• v.21 no.2
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• pp.171-178
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• 2017

In this paper, in order to verify the performance of a localization algorithm using GPS as an auxiliary sensor, the algorithm was applied to a hovering-type autonomous underwater vehicle (AUV) to perform a field test. The applied algorithm is an algorithm to improve the accumulated positional error of dead reckoning using doppler velocity logger(DVL) and tilt-compensated compass module (TCM) mounted on the AUV. GPS when surfaced helps the algorithm to estimate the position and the heading bias error of TCM for geodetic north, which makes it possible to perform dead reckoning on north-east-down (NED) coordinates. As a result of field test performing heading control, it was judged that the algorithm could improve the positional error, enhance the operational capability of AUV and contribute to the research of underwater navigation depending on a magnetic compass.