• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.710-718
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• 2009

The time difference of arrival(TDOA) algorithm is being used widely for identifying the location of a source emanating either electrical or acoustic signal. It's application areas will not be limited to identifying the source at a fixed location, for example the origin of an earthquake, but will also include the trajectory monitoring for a moving source equipped with a GPS sensor. Most of the TDOA algorithm uses time correlation technique to find the time delay between received signals, and therefore difficult to be used for identifying the location of multiple sources. In this paper a TDOA algorithm based on cross-spectrum is developed to find the trajectory of two sound sources with different frequencies. Although its application is limited to for the sources on a disk plane, it can be applied for identifying the locations of more than two sources simultaneously.

• International Journal of Automotive Technology
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• v.8 no.6
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• pp.753-760
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• 2007

In this paper, a roll and pitch estimation algorithm using a set of accelerometers and wireless sensor networks(S/N) is presented for use in a passenger vehicle. While an inertial measurement unit(IMU) is generally used for roll/pitch estimation, performance may be degraded in the presence of longitudinal acceleration and yaw motion. To compensate for this performance degradation, a new roll and pitch estimation algorithm is proposed that uses an accelerometer array, global positioning system(GPS) and in-vehicle networks to get information from yaw rate and roll rate sensors. Angular acceleration and roll and pitch approximation are first calculated based on vehicle kinematics. A discrete Kalman filter is then applied to estimate both roll and pitch more precisely by reducing noise from the running engine and from road disturbance. Finally, the feasibility of the proposed algorithm is shown by comparing its performance experimentally with that of an IMU in the framework of an indoor test platform as well as a test vehicle.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1273-1283
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• 2012

In this paper, vision-based positioning algorithm for melon harvesting robot is presented. RGB value of the input image was converted into HSI value then, melon area was extracted after performing the binarization using HUE value. After morphological filtering was applied to remove noise, outermost boundary points were obtained using border following and convex hull method. Elliptical fitting for melons was perform by the RANSAC algorithm, the center point of ellipse, the length of the short and long axis, and rotation angle were obtained. We verified the effectiveness of the proposed method by various simulation experiments and confirmed actual feasibility of the proposed method by applying to the real melon.

• Journal of Astronomy and Space Sciences
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• v.34 no.1
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• pp.7-17
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• 2017

In South Korea, there are about 80 Global Positioning System (GPS) monitoring stations providing total electron content (TEC) every 10 min, which can be accessed through Korea Astronomy and Space Science Institute (KASI) for scientific use. We applied the computerized ionospheric tomography (CIT) algorithm to the TEC dataset from this GPS network for monitoring the regional ionosphere over South Korea. The algorithm utilizes multiplicative algebraic reconstruction technique (MART) with an initial condition of the latest International Reference Ionosphere-2016 model (IRI-2016). In order to reduce the number of unknown variables, the vertical profiles of electron density are expressed with a linear combination of empirical orthonormal functions (EOFs) that were derived from the IRI empirical profiles. Although the number of receiver sites is much smaller than that of Japan, the CIT algorithm yielded reasonable structure of the ionosphere over South Korea. We verified the CIT results with NmF2 from ionosondes in Icheon and Jeju and also with GPS TEC at the center of South Korea. In addition, the total time required for CIT calculation was only about 5 min, enabling the exploration of the vertical ionospheric structure in near real time.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.1044-1050
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• 2014

Robots used for pipe inspection have been studied for a long time and many mobile mechanisms have been proposed to achieve inspection tasks within pipelines. Localization is an important factor for an inpipe robot to perform successful autonomous operation. However, sensors such as GPS and beacons cannot be used because of the unique characteristics of inpipe conditions. In this paper, an inpipe localization algorithm based on elbow detection is presented. By processing the projected marker images of laser pointers and the attitude and heading data from an IMU, the odometer module of the robot determines whether the robot is within a straight pipe or an elbow and minimizes the integration error in the orientation. In addition, an off-line positioning algorithm has been performed with forward and backward estimation and Procrustes analysis. The experimental environment has consisted of several straight pipes and elbows, and a map of the pipeline has been constructed as the result.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1046-1052
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• 2016

For the safe driving of autonomous vehicles, accurate position estimation is required. Generally, position error must be less than 1m because of lane keeping. However, GPS positioning error is more than 1m. Therefore, we must correct this error and a map matching algorithm is generally used. Especially, road marking intensity map have been used in many studies. In previous work, 3D LIDAR with many vertical layers was used to generate a local intensity map. Because it can be obtained sufficient longitudinal information for map matching. However, it is expensive and sufficient road marking information cannot be obtained in rush hour situations. In this paper, we propose a localization algorithm using an accumulated intensity local map. An accumulated intensity local map can be generated with sufficient longitudinal information using 3D LIDAR with a few vertical layers. Using this algorithm, we can also obtain sufficient intensity information in rush hour situations. Thus, it is possible to increase the reliability of the map matching and get accurate position estimation result. In the experimental result, the lateral RMS position error is about 0.12m and the longitudinal RMS error is about 0.19m.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.5 s.23
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• pp.75-80
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• 2002

Depending upon geographical features and surrounding errors in the survey field, inaccurate positioning is inevitable in a kinematic DGPs survey. Therefore, a data inaccuracy detection algorithm and an interpolation algorithm are essential to meet the requirement of a digital map. In this study, GPS characteristics are taken into account to develop the data inaccuracy detection algorithm. Then, the data interpolation algothim is obtained, based on the feature type of the survey. A digital map for 20km of a rural highway is produced by the kinematic DGPS survey and the features of interests are lines associated with the road. Since the vertical variation of GPS data is relatively higher, the trimmed mean of vertical variation is used as criteria of the inaccuracy detection. Four cases of 0.5%, 1%, 2.5% and 5% trimmings have been experimented. Criteria of four cases are 69cm, 65cm, 61cm and 42cm, respectively. For the feature of a curved line, cublic spine interpolation is used to correct the inaccurate data. When the feature is more or less a straight line, the interpolation has been done by a linear polynomial. Difference between the actual distance and the interpolated distance are few centimeters in RMS.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.335-346
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• 2010

Recently, ESA (European Space Agency) has launched CryoSAT-2 for polar ice observations. CryoSAT-2 is equipped with a SIRAL (SAR/interferometric radar altimeter), which is a high spatial resolution radar altimeter. Conventional altimeters cannot measure a precise three-dimensional ground position because of the large footprint diameter, while SIRAL altimeter system accomplishes a precise three-dimensional ground positioning by means of interferometric synthetic aperture radar technique. In this study, we developed an efficient SIRAL SARIn mode processing technique to measure a precise three-dimensional ground position. We first simulated SIRAL SARIn RAW data for the ideal target by assuming the flat Earth and linear flight track, and second accessed the precision of three-dimensional geopositioning achieved by the proposed algorithm. The proposed algorithm consists of 1) azimuth processing that determines the squint angle from Doppler centroid, and 2) range processing that estimates the look angle from interferometric phase. In the ideal case, the precisions of look and squint angles achieved by the proposed algorithm were about -2.0 ${\mu}deg$ and 98.0 ${\mu}deg$, respectively, and the three-dimensional geopositioning accuracy was about 1.23 m, -0.02 m, and -0.30 m in X, Y and Z directions, respectively. This means that the SIRAL SARIn mode processing technique enables to measure the three-dimensional ground position with the precision of several meters.

• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.116-128
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• 2017

Traffic accidents can be defined as a physical collision event of vehicles occurred instantaneously when drivers do not perceive the surrounding vehicles and roadway environments properly. Therefore, detecting the high potential events that cause traffic accidents with monitoring the interactions among the surroundings continuously by driver is the prerequisite for prevention the traffic accidents. For the analysis, basic data were collected to analyze interactions using a test vehicle which is equipped the GPS(Global Positioning System)-IMU(Inertial Measurement Unit), camera, radar and RiDAR. From the collected data, highway geometric information and the surrounding traffic situation were analyzed and then safety evaluation algorithm for driving vehicle was developed. In order to detect a dangerous event of interaction with surrounding vehicles, locations and speed data of surrounding vehicles acquired from the radar sensor were used. Using the collected data, the tangent and curve section were divided and the driving safety evaluation algorithm which is considered the highway geometric characteristic were developed. This study also proposed an algorithm that can assess the possibility of collision against surrounding vehicles considering the characteristics of geometric road structure. The methodology proposed in this study is expected to be utilized in the fields of autonomous vehicles in the future since this methodology can assess the driving safety using collectible data from vehicle's sensors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.499-501
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• 2016

In this paper, we suggest an advance method for maintaining a perceived behavior as triangle formation and preventing collision between each other in case of a flying drone. In the existing studies, the collision of the drone is only controlled by using light entered in the camera or the image processing. However, when there is no light, it is difficult to confirm the position of each other and they can collide because this system can not confirm the each other's position. Therefore, in this paper, we propose the system to solve the problems by using the distance and the relative coordinates of the three drones that were determined using the ALPS(Ad hoc network Localized Positioning System) algorithm. This system can be a new algorithm that will prevent collisions between each other during flying the drone object. The proposed algorithm is that we make drones maintaining a determined constant value of the distance between coordinates of each drone and the measured center of the drone of triangle formation. Therefore, if the form of fixed formation is disturbed, they reset the position of the drone so as to keep the distance between each drone and the center coordinates constant. As a result of the simulation, if we use the system where the supposed algorithm is applied, we can expect that it is possible to prevent malfunction or an accident due to collisions by preventing collisions of drones in advanced behavior system.