• Journal of Astronomy and Space Sciences
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• v.26 no.1
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• pp.75-88
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• 2009

The main purpose of the current research is to introduce the alternative algorithm of the non-recursive batch filter based on the unscented transformation in which the linearization process is unnecessary. The presented algorithm is applied to the orbit determination of a low earth orbiting satellite and compared its results with those of the well-known Bayesian batch least squares estimation and the iterative UKF smoother (IUKS). The system dynamic equations consist of the Earth's geo-potential, the atmospheric drag, solar radiation pressure and the lunar/solar gravitational perturbations. The range, azimuth and elevation angles of the satellite measured from ground stations are used for orbit determination. The characteristics of the non recursive unscented batch filter are analyzed for various aspects, including accuracy of the determined orbit, sensitivity to the initial uncertainty, measurement noise and stability performance in a realistic dynamic system and measurement model. As a result, under large non-linear conditions, the presented non-recursive batch filter yields more accurate results than the other batch filters about 5% for initial uncertainty test and 12% for measurement noise test. Moreover, the presented filter exhibits better convergence reliability than the Bayesian least squares. Hence, it is concluded that the non-recursive batch filter based on the unscented transformation is effectively applicable for highly nonlinear batch estimation problems.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.1-6
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• 2014

In this study, we propose optimized RBFNNs based on night vision face recognition simulator with the aid of $(2D)^2$ PCA algorithm. It is difficult to obtain the night image for performing face recognition due to low brightness in case of image acquired through CCD camera at night. For this reason, a night vision camera is used to get images at night. Ada-Boost algorithm is also used for the detection of face images on both face and non-face image area. And the minimization of distortion phenomenon of the images is carried out by using the histogram equalization. These high-dimensional images are reduced to low-dimensional images by using $(2D)^2$ PCA algorithm. Face recognition is performed through polynomial-based RBFNNs classifier, and the essential design parameters of the classifiers are optimized by means of Differential Evolution(DE). The performance evaluation of the optimized RBFNNs based on $(2D)^2$ PCA is carried out with the aid of night vision face recognition system and IC&CI Lab data.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.444-450
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• 2015

The accuracy of small and low cost CCD camera is insufficient to provide data for precisely tracking unmanned aerial vehicles(UAVs). This study shows how UAV can hover on a human targeted tracking object by using CCD camera rather than imprecise GPS data. To realize this, UAVs need to recognize their attitude and position in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for an UAV to estimate of his attitude by environment recognition for UAV hovering, as one of the best important problems. In this paper, we describe a method for the attitude of an UAV using image information of a maker on the floor. This method combines the observed position from GPS sensors and the estimated attitude from the images captured by a fixed camera to estimate an UAV. Using the a priori known path of an UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a marker on the floor and the estimated UAV's attitude. Since the equations are based on the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the UAV. The Kalman filter scheme is applied for this method. its performance is verified by the image processing results and the experiment.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.84-91
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• 2009

This paper presents an online partial 3D modeling methodology that uses a mobile augmented reality system and aerial photographs, and a tracking methodology that compares the 3D model with a video image. Instead of relying on models which are created in advance, the system generates a 3D model for a real building on the fly by combining frontal and aerial views. A user's initial pose is estimated using an aerial photograph, which is retrieved from a database according to the user's GPS coordinates, and an inertial sensor which measures pitch. We detect edges of the rooftop based on Graph cut, and find edges and a corner of the bottom by minimizing the proposed cost function. To track the user's position and orientation in real-time, feature-based tracking is carried out based on salient points on the edges and the sides of a building the user is keeping in view. We implemented camera pose estimators using both a least squares estimator and an unscented Kalman filter (UKF). We evaluated the speed and accuracy of both approaches, and we demonstrated the usefulness of our computations as important building blocks for an Anywhere Augmentation scenario.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.90-100
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• 2017

The purpose of this study is to detect and label the vehicles using the drone images as a way to overcome the limitation of the existing point and section detection system and vehicle gap estimation on Arterial road. In order to select the appropriate time zone, position, and altitude for the acquisition of the drone image data, the final image data was acquired by shooting under various conditions. The vehicle was detected by applying mixed Gaussian, image binarization and morphology among various image analysis techniques, and the vehicle was labeled by applying Kalman filter. As a result of the labeling rate analysis, it was confirmed that the vehicle labeling rate is 65% by detecting 185 out of 285 vehicles. The gap was calculated by pixel unitization, and the results were verified through comparison and analysis with Daum maps. As a result, the gap error was less than 5m and the mean error was 1.67m with the preceding vehicle and 1.1m with the following vehicle. The gaps estimated in this study can be used as the density of the urban roads and the criteria for judging the service level.

• Proceedings of the International Union of Geodesy And Geophysics Korea Journal of Geophysical Research Conference
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• 2003.05a
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• pp.19-19
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• 2003

본 연구에서는 국립해양조사원의 '해양2000호'를 통해 1996년과 1997년에 측정한 동해 지역의 중력자료에 대한 자료 처리를 하였다. 효과적인 자료처리를 위해 선상중력자료 처리에 필요한 각종 보정 절차와 문제점 등을 알아보았으며, 선상자료와 해면고도계자료의 비교 및 이를 통한 선상자료의 검증을 실시하였다. 선상중력자료는 측정과 처리 과정에 있어 여러 사항을 고려하여야 한다. 즉, 육상중력기점을 이용한 절대중력으로의 환산 문제, 선박 항해 위치의 부정확성에 기인하는 문제 및 중력계의 기계적 특성과 중력 측정이 이루어질 때의 해상 조건에 의한 영향 등으로 선상중력자료에 나타나는 여러 오차를 최소화하여야 한다. 선상중력자료로부터 각종 지구중력장 연구에 필요한 중력이상을 계산하기 위해 선상중력 측정시 기인되는 각종 요인의 오차를 고려한 효과적인 보정이 이루어져야 한다. 즉, 선상중력계의 기계변이 보정, 탐사선에 대한 위치 자료의 획득 및 필터링, 그리고 탐사선의 이동으로 인한 Eotvos 효과의 정확한 계산 및 보정이 필요하고, 선상중력계의 기계적 특성에 의해 나타나는 시간지연에 대한 보정도 필요하다. 또한 이러한 보정을 통해 계산한 중력 이상에서 각 교점의 오차를 보정하는 교정오차 보정도 실시하여야 한다. 특히, 탐사선의 이동으로 인한 지구자전 각속도의 상대적인 증감의 효과로 나타나는 Eotvos 효과의 영향은 선상중력자료의 정확도에 가장 큰 영향을 미친다. 이의 정확한 계산 및 보정을 위해서는 정확한 위치정보가 필요하며 본 연구에서는 이를 위해 GPS 항해정보에 대한 Kalman 필터를 실시하였고, Eotvos 효과에 대해 Savitzky-Golay 필터를 적용하여 최적의 Eotvos 보정을 시도하였다. 본 연구에서 계산된 동해지역의 중력이상에 대한 대력적인 범위는 경도 129° - 133°이고 위도 35° - 38.3° 부근이다. 이 지역에 대한 고도이상은 최소 -42.46 mGal에서 최고 161.13 mGal사이에 분포하며, 고도이상의 평균은 14.450 mGal이다. 또한 Bouguer 이상은 최소 -l5.09 mGal에서 최고 218.61 mGal이고 이의 평균은 82.681 mGal이다. 그리고 동해지역의 선상중력 측정지역에서 선상자료에 의한 중력이상과 Altimeter 자료에 의한 고도이상의 전반적인 윤곽은 비슷하면서도 일부 작은 이상의 차이가 나타났으며, 지형자료와 비교하여 보면 Altimeter에 의한 결과보다 선상측정에 의한 결과가 더욱 잘 일치하고 있어 본 연구에서 계산한 선상자료의 타당성을 알 수 있다. 고도이상의 차이는 최소 -25.94 mGal에서 최대 85.33 mGal의 차이를 보이며 차이의 평균은 3.517 mGal, RMS는 6.774 meal이다. 이는 비교적 큰 차이로 선상측정자료의 중요성과 필요성을 단적으로 나타내고 있다.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.802-810
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• 2006

For a thorough train control, the precise train position detection is necessarily required. The widely used current way for train position detection is the one of using track circuits. The track circuit has a simple structure, and has a high level of reliability. However trains can be detected only on track circuits, which have to be installed on all ground sections, and much amount of cost for its installation and maintenance is needed. In addition, for the track circuit, only discontinuous position detection is possible because of the features of the closed circuit loop configuration. As the recent advances in telecommunication technologies and high-tech vehicle-based control equipments, for the train position detection, the method to detect positions directly from on trains is being studied. Vehicle-based position detection method is to estimate train positions, speed, timing data continuously, and to use them as the control information. In this paper, the features of GPS navigation and DR navigation are analyzed, and the navigation filters are designed by constructing vehicle-based train position detection method by combining GPS navigation and DR navigation for their complementary cooperation, and by using kalman filter. The position estimation performance of the proposed method is also confirmed by simulations.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.12
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• pp.1032-1039
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• 2012

The present paper deals with precise relative positioning of formation satellites with long baseline in low Earth orbit making use of L1/L2 dual frequency GPS carrier phase measurements. Kinematic approach means to describe the motion of objects without taking its mass/dynamics model into consideration. The advantage of the kinematic approach is that information about dynamics of the system is not applied, which gives more flexibility and could improve the scientific interest of the observations made by the mission. The ionosphere terms, which are not canceled by double differenced measurement equation in the case of the long baseline, are explicitly estimated as unknown parameters by extended Kalman filter. The estimated float ambiguities by EKF are solved by existing efficient integer vector search strategy under integer least square condition. For the integer vector search, we employ well known MLAMBDA. Finally, The feasibility and accuracy of processing scheme are demonstrated using the GPS measurements for two satellites in low Earth orbit separated by baselines of 100 km.

• Journal of Navigation and Port Research
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• v.37 no.6
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• pp.575-580
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• 2013

The IMU(Inertial Measurement Unit) which is the expensive equipment has been used as a special limited area, usually in measurement of posture of applying to the areas of ship, submarine, aircraft and military equipment application. However, in the current situation, MEMS AHRS technology can replace the high-priced IMU in MEMS AHRS selected application field. In this paper, wireless hull motion measurement system was suggested for measuring key elements of ship's movement such as rolling, pitching and yawing using gyro, acceleration and magnetic sensors of AHRS. In order to reduce the error such as instantaneous acceleration, effects and vibration of geomagnetic, we have adopted the sensors equipped with Kalman filtering. The Wireless hull motion measurement system using AHRS sensors was tested in actual ship and it could easily be applied in limited installation circumstances of the ship. In the future, this system can be useful in the navigation safety and marine accident analysis by using with ship equipment such as INS or VDR in the maritime.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.609-617
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• 2016

In general, Personal Navigation Systems (PNSs) can be defined systems to acquire pedestrian positional information. GPS is an example of PNS. However, GPS can only be used where the GPS signal can be received. Pedestrian Dead Reckoning (PDR) can estimate the positional information of pedestrians using Inertial Measurement Unit (IMU). Therefore, PDR can be used for GPS-disabled areas. This paper proposes a PDR scheme considering various movement types over GPS-disabled areas as combat environments. We propose a movement distance estimation scheme and movement direction estimation scheme as pedestrian's various movement types such as walking, running and crawling using IMU. Also, we propose a fusion algorithm between GPS and PDR to mitigate the lack of accuracy of positional information at the entrance to the building. The proposed algorithm has been tested in a real test bed. In the experimental results, the proposed algorithms exhibited an average position error distance of 5.64m and position error rate in goal point of 3.41% as a pedestrian traveled 0.6km.