• Journal of Sensor Science and Technology
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• v.16 no.4
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• pp.294-300
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• 2007

Autonomous unmanned vehicle is able to find the path and the way point by itself. For the more precise navigation performance, Extended kalman filter, which is integrated with inertial navigation system and global positioning system is proposed in this paper. Extended kalman filter's performance is evaluated by the simulation and applied to the unmanned vehicle. The test result shows the effectiveness of extended kalman filter for the navigation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.424-431
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• 2017

These days land vehicle navigation system is a subject of great interest. The GNSS(Global Navigation Satellite System) is the most popular technology for out door positioning. However, The GNSS is incapable of providing high accuracy and reliable positioning. For that reason, we applied Network-RTK in vehicle to improve the accuracy of GNSS performance. In this network-RTK mode, the GNSS error are significantly decreased. In this paper, we explain ntrip client program for network-RTK mode and show the result of experiments in various environments.

• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.800-807
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• 2010

Jack knifing accident of semi trailer vehicle is one of the most dangerous accident type because the vehicle cross over its lane by the accident. Jack knifing accident can be predicted and detected by GNSS precise relative positioning. But integer ambiguity resolution procedure is inevitable in GNSS precise relative positioning. In this paper, success rate improving method of integer ambiguity resolution is proposed for jack knifing accident prediction and detection of semi trailer vehicle, and proposed method is tested by simulation.

• 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 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.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.112-119
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• 2023

In urban environments, signals of Global Positioning System (GPS) can be blocked and reflected by tall buildings, large vehicles, and complex components of road network. Therefore, the performance of the positioning system using the GPS module in urban areas can be degraded due to the loss of GPS signals necessary for the position estimation. To deal with this issue, various localization schemes using inertial measurement unit (IMU) sensors, such as gyroscope and accelerometer, and Bayesian filters, such as Kalman filter (KF) and particle filter (PF), have been designed to enhance the performance of the GPS-based positioning system. Among Bayesian filters, the PF has been widely used for the target tracking and vehicle navigation, since it can provide superior performance in estimating the state of a dynamic system under nonlinear/non-Gaussian circumstance. This paper presents a positioning system that uses the double-stacked particle filter (DSPF) as well as the accelerometer, gyroscope, and GPS receiver on the smartphone to provide higher pedestrian positioning accuracy in urban environments. The DSPF employs a nonparametric technique (Parzen-window) to create the multimodal target distribution that approximates the posterior distribution. Experimental results show that the DSPF-based positioning system can provide the significant improvement of the pedestrian position estimation in urban environments.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.3
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• pp.92-101
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• 2010

Energy efficiency and safe mobility are the two key constituents of the future automobile. The technologies that enable these features are now heavily dependent upon information and communication technology rather than traditional auto-mechanical technology. This paper presents an exploratory project 'Smart&Green Vehicle Project' at Western Michigan University which is to improve the geographical location accuracy of vehicles and to study various applications of making such location data available. Global Positioning System (GPS), Inertial Navigation System (INS), Vehicular Ad-hoc Network (VANET) technology, and data fusion among these technologies are investigated. Testing and evaluation is done on systems which will gather vehicular positioning data during GPS signal loss. Vehicles in urban settings do not acquire accurate positioning data from GPS alone; therefore there is a need for exploration into technology that can assist GPS in urban settings. The goal of this project is to improve the accuracy of positioning data during a loss of GPS signal. Controlled experiments are performed to gather data which aided in assessing the feasibility of these technologies for use in vehicular platforms.

• 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.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.88-92
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• 2018

Currently drone industry has become one of the fast growing markets and the technology for unmanned aerial vehicles are expected to continue to develop at a rapid rate. Especially small unmanned aerial vehicle systems have been designed and utilized for the various field with their own specific purposes. In these fields the path planning problem to find the shortest path between two oriented points is important. In this paper we introduce a path planning strategy for an autonomous flight of unmanned aerial vehicles through reinforcement learning with self-positioning technique. We perform Q-learning algorithm, a kind of reinforcement learning algorithm. At the same time, multi sensors of acceleraion sensor, gyro sensor, and magnetic are used to estimate the position. For the functional evaluation, the proposed method was simulated with virtual UAV environment and visualized the results. The flight history was based on a PX4 based drones system equipped with a smartphone.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.491-494
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• 2006

Kalman filtering (KF) is hard to be applied to the SINS (Strap-down Inertial Navigation System)/RDSS (Radio Determination Satellite Service) integrated navigation system directly because the time delay of RDSS positioning in active mode is random. BP (Back-Propagation) Neuron computing as a powerful technology of Artificial Neural Network (ANN), is appropriate to solve nonlinear problems such as the random time delay of RDSS without prior knowledge about the mathematical process involved. The new algorithm betakes a BP neural network (BPNN) and velocity feedback to aid KF in order to overcome the time delay of RDSS positioning. Once the BP neural network was trained and converged, the new approach will work well for SINS/RDSS integrated navigation. Dynamic vehicle experiments were performed to evaluate the performance of the system. The experiment results demonstrate that the horizontal positioning accuracy of the new approach is 40.62 m (1 ${\sigma}$), which is better than velocity-feedback-based KF. The experimental results also show that the horizontal positioning error of the navigation system is almost linear to the positioning interval of RDSS within 5 minutes. The approach and its anti-jamming analysis will be helpful to the applications of SINS/RDSS integrated systems.