• 한국항해학회지
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• 제21권2호
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• pp.59-67
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• 1997

This paper describe the method and the result of making a fiber optic gyrocompass measuring the heading angles of a ship with a fiber optic sensor. As the method seeking for the heading angles, it is possible to get the heading angles by measuring the output signals from a stationary fiber optic sensor in at least three directions such as a heading direction and other two directions having phase difference ${\phi}1$ and ${\phi}2$ to the heading. We made the static fiber optic gyrocompass by a high performance fiber optic sensor having scale factor of 210mV/deg/s and resolution of 0.5deg/hr using this principle. The accuracy of this system was $0.29^{\circ}$ from 20 numbers of data measuring the arbitrary heading angle.

• Journal of Advanced Marine Engineering and Technology
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• 제39권5호
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• pp.522-526
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• 2015

Generally, underwater unmanned vehicle have adopted an inertial navigation system (INS), dead reckoning (DR), acoustic navigation and geophysical navigation techniques as the navigation method because GPS does not work in deep underwater environment. Even if the tactical inertial sensor can provide very detail measurement during long operation time, it is not suitable to use the tactical inertial sensor for small size and low cost UUV because the tactical inertial sensor is expensive and large. One alternative to INS is attitude heading reference system (AHRS) with the micro-machined electro mechanical system (MEMS) inertial sensor because of MEMS inertial sensor's small size and low power requirement. A cost effective and small size attitude heading reference system (AHRS) which incorporates measurements from 3-axis micro-machined electro mechanical system (MEMS) gyroscopes, accelerometers, and 3-axis magnetometers has been developed to provide a complete attitude solution for UUV. The AHRS based MEMS overcome many problems that have inhibited the adoption of inertial system for small UUV such as cost, size and power consumption. Several evaluation experiments were carried out for the validation of the developed AHRS's function and these experiments results are presented. Experiments results prove the fact that the developed MEMS AHRS satisfied the required specification.

• 전자공학회논문지
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• 제53권7호
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• pp.95-102
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• 2016

쿼드로터의 헤딩 방향 감지를 위하여 일반적으로 활용하는 지자기 센서에서는 주변 자기 간섭 및 롤/피치 축 기울기로 인한 오차가 발생한다. 본 논문에서는 쿼드로터 헤딩 방향 제어를 위하여 지자기 센서와 자이로 센서의 측정 결과를 융합하는 방위각 측정 방법을 제안한다. 롤/피치 축 방향 회전으로 인하여 발생하는 지자기 센서의 좌표축 변화를 분석하고, 수평 자세 제어를 목적으로 측정된 롤/피치 축 각도를 활용하여 지자기 센서의 기울기 보상을 적용한다. 또한, 요 축 각도 측정에 상보필터를 활용하여 지자기 센서의 요 축 각도와 자이로스코프 센서의 요 축 방향 각속도 데이터를 융합한다. 제안한 방식을 실험에 적용하고 결과를 제시하여 요 축 각도 측정의 타당성 및 효과를 검증한다.

• 로봇학회논문지
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• 제15권1호
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• pp.16-23
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• 2020

This paper describes an alignment algorithm that estimates the initial heading angle of AUVs (Autonomous Underwater Vehicle) for starting navigation in a sea area. In the basic dead reckoning system, the initial orientation of the vehicle is very important. In particular, the initial heading value is an essential factor in determining the performance of the entire navigation system. However, the heading angle of AUVs cannot be measured accurately because the DCS (Digital Compass) corrupted by surrounding magnetic field in pointing true north direction of the absolute global coordinate system (not the same to magnetic north direction). Therefore, we constructed an experimental constraint and designed an algorithm based on extended Kalman filter using only inertial navigation sensors and a GPS (Global Positioning System) receiver basically. The value of sensor covariance was selected by comparing the navigation results with the reference data. The proposed filter estimates the initial heading angle of AUVs for navigation in a sea area and reflects sampling characteristics of each sensor. Finally, we verify the performance of the filter through experiments.

• Journal of Hydrospace Technology
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• 제1권1호
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• pp.75-88
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• 1995

Auto-Pilot System uses heading angle information via the position sensor and the rudder device to control the ship's direction. Most of the control logics are composed of the state estimation and control algorithms assuming that the measurement device and the actuator have no fault except the measurement noise. But such asumptions could bring the danger in real situation. For example, if the heading angle measuring device is out of order the control action based on those false position information could bring serious safety problem. In this study, the control system including improved method for processing the position information is applied to the Auto-Pilot System. To show the difference between general state estimator and F.D.F., BJDFs for the sensor and the actuator failure detection are designed and the performance are tested. And it is shown that bias error in sensor could be detected by state-augmented estimator. So the residual confined in the 2-dimension in the presence of the sensor failure could be unidirectional in output space and bias sensor error is much easier to be detected.

• 대한기계학회논문집
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• 제16권11호
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• pp.2021-2032
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• 1992

본 연구에서는 이동 로보트의 구동모터들의 회전수를 측정하는 두 개의 엔코 더와 로보트의 회전각 속도를 측정하는 자이로센서를 결합하여 주행중인 로보트의 자 세를 정확하게 추정할 수 있는 복수센서 시스템의 신호처리회로 및 알고리즘을 개발하 고 자이로센서의 측정방정식을 모델링하기 위하여 성능시험을 수행하였다. 그리고 확률이론을 유도된 측정방정식에 적용하여 본 복수센서 시스템의 출력 신호들을 효율 적으로 융합할 수 있는 센서데이터 융합알고리즘을 개발하여 사용된 측정센서들에 내 재하는 측정오차의 영향을 최소로 줄이고자 하였다. 제안된 융합알고리즘의 타당성 을 검증하기 위하여 주행실험을 수행하여 이동 로보트의 실제자세와 본 융합알고리즘 의 결과를 비교하였다.

• 한국군사과학기술학회지
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• 제8권4호
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• pp.14-23
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• 2005

In this paper we propose a sensor fusion method for the navigation algorithm which can be used to estimate state vectors such as position and velocity for its motion control using multi-sensor output measurements. The output measurement we will use in estimating the state is a series of known multi-sensor asynchronous outputs with measurement noise. This paper investigates the Extended Kalman Filtering method to merge asynchronous heading, heading rate, velocity of DVL, and SSBL information to produce a single state vector. Different complexity of Kalman Filter, with. biases and measurement noise, are investigated with theoretically data from MOERI's SAUV. All levels of complexity of the Kalman Filters are shown to be much more close and smooth to real trajectories then the basic underwater acoustic navigation system commonly used aboard underwater vehicle.

• 센서학회지
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• 제19권4호
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• pp.278-284
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• 2010

Indoor localization for pedestrian is the key technology for caring the elderly, the visually impaired and the handicapped in health care districts. It also becomes essential for the emergency responders where the GPS signal is not available. This paper presents newly developed pedestrian localization system using the gyro sensors, the magnetic compass and pressure sensors. Instead of using the accelerometer, the pedestrian gait is estimated from the gyro sensor measurements and the travel distance is estimated based on the gait kinematics. Fusing the gyro information and the magnetic compass information for heading angle estimation is presented with the error covariance analysis. A pressure sensor is used to identify the floor the pedestrian is walking on. A complete ambulatory system is implemented which estimates the pedestrian's 3D position and the heading.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1984년도 추계학술발표회 논문집
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• pp.100-106
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• 1984

Traditional methods for estimating the location of underwater target, i.e. the triangulation method and the wavefront curvature method, have been utilized. The location of a target is defined by the range and the bearing, which estimates can be obtained by evaluating the time delay between neighboring sensors. Many components of error occur in estimating the target range, among which the error due to the fluctuation of heading angle is outstanding. In this paper, the wavefront curvature method was used. We considered the error due to the heading fluctuation as the $\beta$-density process, from which we analized the range estimates with $\beta$-density function exist in some finite limits, and its mean value and variation are depicted as a function of true range and heading fluctuation. Given heading angles and sensor separation, maximum estimated heading errors are presented as a function of true range.

• Journal of Biosystems Engineering
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• 제25권1호
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• pp.55-62
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• 2000

Autonomous farm operation needs to be developed for safety, labor shortage problem, health etc. In this research, an autonomous tractor for tillage was investigated using machine vision and a fuzzy logic controller(FLC). Tractor heading and offset were determined by image processing and a geomagnetic sensor. The FLC took the tractor heading and offset as inputs and generated the steering angle for tractor guidance as output. A color CCD camera was used fro the image processing . The heading and offset were obtained using Hough transform of the G-value color images. 15 fuzzy rules were used for inferencing the tractor steering angle. The tractor was tested in the file and it was proved that the tillage operation could be done autonomously within 20 cm deviation with the machine vision and the FLC.