• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2004.05a
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• pp.249-256
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• 2004

KRISO/KORDI is currently developing a deep-sea unmanned underwater vehicle (UUV) system which is composed of a launcher, an ROV, and an AUV. Two USBL acoustic positioning systems will be used for UUV's navigation. One is for the deep sea positioning of all three vehicles and the other is for AUV's guidance to the docking device on the launcher. In order to increase the position accuracy MFSK(Multiple Frequency Shift Keying) broadband signal will be used. As the first step to the implementation of a USBL system, this paper studies USBL positioning algorithm using MFSK signals. Firstly, the characteristics of MFSK signal is described with various MFSK parameters: number of frequencies, frequency step, center frequency, and pulse length. Time and phase delays between two received signals are estimated by using cross-correlation and cross-spectrum methods. Finally an USBL positioning algorithm is derived by converting the delays to difference of distances and applying trigonometry. The simulation results show that the position accuracy is improved highly when both cross-correlation and cross-spectrum of MFSK signals are used simultaneously.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.4
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• pp.51-58
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• 2003

Satellite-based positioning system such as global positioning system(GPS) has played a major role in data capture technology for constructing GIS database. Recent advances in satellite-based positioning technology have made the task of precisely locating features fast, easy, and inexpensive, and determined their current latitude and longitude. However, there are still situations where satellite-based positioning service will not provide users with desired precision such as in urban environments, that is, the only severe handicap still hampering satellite-based positioning is the well-known problem of restricted satellite visibilities. As the majority of the creation and updating of road and street network are carried out in urban environments, the obstruction problem considerably impedes the wider application of satellite-based positioning. This paper presents the current GPS-based positioning environment for GIS data acquisition in urban areas. A field experiment with measurement vehicle has been performed under varying operational conditions and areas where shading of satellite signal is encountered due to buildings and overpasses with measurement vehicle in order to evaluate the availability of existing GPS-based positioning. We found that the current GPS-base positioning system we used in this study was insufficient for a precise GIS data acquisition. This research would make a contribution for the development of base data to supplementary technology, which can complement the existing GPS-based positioning.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2075-2077
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• 2001

For precise positioning, GPS carrier measurements are often used. In this case, accurate position having mm${\sim}$cm error can be obtained. For 3D positioning, in CDGPS, more than five carrier phase measurements are required. When GPS signals are blocked or carrier phase measurements are insufficient, it cannot provide positioning solution. By integrating CDGPS with INS, continuity of positioning solution can be guaranteed. However, when a vehicle moves in low speed or in stationary, the CDGPS/INS integrated system is difficult to compensate INS attitude errors because GPS velocity error become relatively lange. In this paper, we used the 3D attitude GPS receiver to compensate the INS attitude error. By field experiments, it is shown that the proposed integration system maintains the navigation performance even when a vehicle is in low speed or GPS signal is blocked for a period of time.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.2
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• pp.98-109
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• 2010

This paper deals with a new method of unmanned aerial vehicle (UAV) recovery when a UAV fails to get a global positioning system (GPS) signal at an unprepared site. The proposed method is based on the simultaneous localization and mapping (SLAM) algorithm. It is a process by which a vehicle can build a map of an unknown environment and simultaneously use this map to determine its position. Extensive research on SLAM algorithms proves that the error in the map reaches a lower limit, which is a function of the error that existed when the first observation was made. For this reason, the proposed method can help an inertial navigation system to prevent its error of divergence with regard to the vehicle position. In other words, it is possible that a UAV can navigate with reasonable positional accuracy in an unknown environment without the aid of GPS. This is the main idea of the present paper. Especially, this paper focuses on path planning that maximizes the discussed ability of a SLAM algorithm. In this work, a SLAM algorithm based on extended Kalman filter is used. For simplicity's sake, a blimp-type of UAV model is discussed and three-dimensional pointed-shape landmarks are considered. Finally, the proposed method is evaluated by a number of simulations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.1
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• pp.94-106
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• 2020

Recently, the radio navigation method utilizing the GPS(Global Positioning System) satellite information is widely used as the method to measure the position of objects. As GPS applications become wider and fields based on various positioning information emerge, new methods for achieving higher accuracy are required. In the case of autonomous vehicles, the INS(Inertial Navigation System) using the IMU(Inertial Measurement Unit), and the DR(Dead Reckoning) algorithm using the in-vehicle sensor, are used for the purpose of preventing degradation of accuracy of the GPS and to measure the position in the shadow area. However, these positioning methods have many elements of problems due not only to the existence of various shaded areas such as building areas that are continually enlarged, tunnels, underground parking lots and but also to the limitations of accumulation-based location estimation methods that increase in error over time. In this paper, an efficient positioning method in a large underground parking space using Fingerprint method is proposed by placing the AP(Access Points) and directional antennas in the form of four anchors using WLAN, a popular means of wireless communication, for positioning the vehicle in the GPS shadow area. The proposed method is proved to be able to produce unchanged positioning results even in an environment where parked vehicles are moved as time passes.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.4
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• pp.244-250
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• 2008

This paper describes the design and implementation of an unmanned ground vehicle (UGV) and also estimates how well autonomous navigation and remote control of UGV can be performed through the optimized arbitration of several sensor data, which are acquired from vision, obstacle detection, positioning system, etc. For the autonomous navigation, lane detection and tracing, global positioning, and obstacle avoidance are necessarily required. In addition, for the remote control, two types of experimental environments are established. One is to use a commercial racing wheel module, and the other is to use a haptic device that is useful for a user application based on virtual reality. Experimental results show that autonomous navigation and remote control of the designed UGV can be achieved with more effectiveness and accuracy using the proper arbitration of sensor data and navigation plan.

• Journal of Power Electronics
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• v.6 no.3
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• pp.264-270
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• 2006

Magnetic levitation transfer systems are useful for transfer tools in clean rooms and positioning control systems with high precision because of frictionless characteristics. In this paper, the new method is proposed which is a sensorless position. At first, the magnetic levitation is performed by state feedback control with a disturbance observer for each of six axes of the movement of a levitated vehicle. The position of the vehicle is then estimated as the disturbance term of a disturbance observer for a pitch angle which is one of the control axes for the magnetic levitation. In addition, the positioning force is generated by the pitch angle control which gives a tilt to the levitated vehicle so that it generates the horizontal component of force.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.3
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• pp.107-117
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• 2019

When a vehicle moves with a high rotation rate, it is not easy to measure the angular velocity using an off-the-shelf gyroscope. If the angular velocity is estimated using the extended Kalman filter in the gyro-free inertial navigation system, the effect of the accelerometer error and initial angular velocity error can be reduced. In this paper, in order to improve the navigation performance of the gyro-free inertial navigation system, an angular velocity estimation method is proposed based on an extended Kalman filter with an accelerometer random bias error model. In order to show the validity of the proposed estimation method, angular velocities and navigation outputs of a vehicle with 3 rev/s rotation rate are estimated. The results are compared with estimates by other methods such as the integration and an extended Kalman filter without an accelerometer random bias error model. The proposed method gives better estimation results than other methods.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.154-159
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• 2014

The Global Positioning System (GPS) is widely used to aid the navigation of aerial vehicles. However, the GPS cannot be used indoors, so alternative navigation methods are needed to be developed for micro aerial vehicles (MAVs) flying in GPS-denied environments. In this paper, a real-time three-dimensional (3-D) indoor navigation system and closed-loop control of a quad-rotor aerial vehicle equipped with an inertial measurement unit (IMU) and a low-cost light detection and ranging (LIDAR) is presented. In order to estimate the pose of the vehicle equipped with the two-dimensional LIDAR, an octree-based grid map and Monte-Carlo Localization (MCL) are adopted. The navigation results using the MCL are then evaluated by making a comparison with a motion capture system. Finally, the results are used for closed-loop control in order to validate its positioning accuracy during procedures for stable hovering and waypoint-following.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.2
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• pp.97-103
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• 2005

Most car navigation systems(CNS) estimate the vehicle's location using global positioning system(GPS) or dead reckoning(DR) system. However, the estimated location has undesirable errors because of various noise sources such as unpredictable GPS noises. As a result, the measured position is not lying on the road, although the vehicle is known to be restricted on the road network. The purpose of map matching is to locate the vehicle's position on the road network where the vehicle is most likely to be positioned. In this paper, we analyze some general map matching algorithms first. Then, we propose a map matching method using compensation vectors to improve the performance of map matching. The proposed method calculates a compensation vector from the discrepancy between a measured position and an estimated position. The compensation vector and a newly measured position are to be used to determine the next estimation. To show the performance improvement of the map matching using compensation vectors, the real time map matching experiments are performed. The real road experiments demonstrate the effectiveness and applicability of the proposed map matching.