• Journal of KIISE:Databases
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• v.29 no.6
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• pp.441-452
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• 2002

If continuous moving objects are managed by conventional database, it is not possible for them to store all position information changed over time in the database. Therefore, a time period of regular rate is determined and position information of moving objects are discretely stored in the system for every time period. However, if continuous moving objects are managed as discrete model, we will have problems which cannot properly answer to the query about uncertain past or future position information. To solve this problem, in this paper, we propose the method and algorithm which use the history information stored in the same database, to estimate the past or future location of moving objects. The cubic spline interpolation is used to estimate the past location and the mean movement value of the history information is used to predict the future location of moving objects. Finally, from the location estimation experimentation of using virtual trajectory and location sample, we proved that the proposed cubic spline function has less error than the linear function.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.211-217
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• 2019

Location positioning systems are available in applications such as mobile, robotic tracking systems and Wireless location-based service (LBS) applications. The GPS system is the most well-known location tracking system, but it is not easy to use indoors. The method of radio frequency identification (RFID) location tracking was studied in terms of cost-effectiveness for indoor location tracking systems. Most RFID systems use active RFID tags using expendable batteries, but in this paper, an inexpensive indoor location tracking system using passive RFID tags has been developed. A pattern matching method and a system for tracing location by generating regression curves were studied to use precision tracking algorithms. The system was tested by verifying the level of error caused by noise. The three-dimensional curves are produced by the regression equation estimated the statistically meaningful coordinates by the differential equation. The proposed system could also be applied to mobile robot systems, AGVs and mobile phone LBSs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1549-1556
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• 2016

By increased moving speed of aircraft, estimating location of itself becomes more important than ever. This requirement is satisfied by appearance of GPS, however it is useless when signal reception from satellite is not good enough by interruption, for example, traffic jamming. Applying link for communication to additional positioning system is capable of providing relative position of aircraft. Estimating location with link for communication is done without additional equipment but with signal processing based on correlation of received signal. ESPRIT is one of the representative algorithm among them. Estimating correlation matrix is possible to have error since it includes average operation needs enough number of samples not impractical. Therefore we propose algorithm that defines, estimates and removes error matrix of correlation. Proposing algorithm shows better performance than previous one when transmitters are close.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.173-180
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• 2006

A key component of autonomous navigation of intelligent home robot is localization and map building with recognized features from the environment. To validate this, accurate measurement of relative location between robot and features is essential. In this paper, we proposed relative localization algorithm based on 3D reconstruction of scale invariant features of two images which are captured from two parallel cameras. We captured two images from parallel cameras which are attached in front of robot and detect scale invariant features in each image using SIFT(scale invariant feature transform). Then, we performed matching for the two image's feature points and got the relative location using 3D reconstruction for the matched points. Stereo camera needs high precision of two camera's extrinsic and matching pixels in two camera image. Because we used two cameras which are different from stereo camera and scale invariant feature point and it's easy to setup the extrinsic parameter. Furthermore, 3D reconstruction does not need any other sensor. And the results can be simultaneously used by obstacle avoidance, map building and localization. We set 20cm the distance between two camera and capture the 3frames per second. The experimental results show :t6cm maximum error in the range of less than 2m and ${\pm}15cm$ maximum error in the range of between 2m and 4m.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.109-117
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• 2010

With the advent of new technologies such as HSDPA, WiBro(Wireless Broadband) and personal devices, we can access various contents and services anytime and anywhere. A location based service(LBS) is essential for providing personalized services with individual location information in ubiquitous computing environment. In this paper, we propose mapping algorithm for error compensation of indoor localization system. Also we explain filter and indoor localization system. we have developed mapping algorithms composed of a map recognition method and a position compensation method. The map recognition method achieves physical space recognition and map element relation extraction. We improved the accuracy of position searching. In addition, we reduced position errors using a dynamic scale factor.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.700-710
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• 2009

A computer vision technique of estimating the location of an unmanned ground vehicle is proposed. Identifying the location of the unmaned vehicle is very important task for automatic navigation of the vehicle. Conventional positioning sensors may fail to work properly in some real situations due to internal and external interferences. Given a DSM(Digital Surface Map), location of the vehicle can be estimated by the registration of the DSM and multiview range images obtained at the vehicle. Registration of the DSM and range images yields the 3D transformation from the coordinates of the range sensor to the reference coordinates of the DSM. To estimate the vehicle position, we first register a range image to the DSM coarsely and then refine the result. For coarse registration, we employ a fast random sample matching method. After the initial position is estimated and refined, all subsequent range images are registered by applying a pair-wise registration technique between range images. To reduce the accumulation error of pair-wise registration, we periodically refine the registration between range images and the DSM. Virtual environment is established to perform several experiments using a virtual vehicle. Range images are created based on the DSM by modeling a real 3D sensor. The vehicle moves along three different path while acquiring range images. Experimental results show that registration error is about under 1.3m in average.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.55-63
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• 2013

In cognitive radio networks, acquirement of position information of primary user is very important to secondary network since localization information of primary users can be utilized for improving the spectrum efficiency of secondary network and for avoiding harmful interference to primary users by using proper power control. Among various location methods, Received Signal Strength (RSS)-based localization has been widely used for distance measurements in the location detection process despite its inherent inaccuracy because it can be easily implemented without any additional hardware cost. In the RSS-based localization, the distance is measured by the received signal strength, and distance error can be caused by many factors such as fading, shadowing and obstacle between two nodes. In the paper, therefore we propose a localization scheme based on estimation of path-loss exponent to localize the location of primary users more accurately. Through simulations, it is shown that the proposed scheme can provide less localization error and interference rate to primary users than other schemes.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.101-108
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• 2019

Depending on the location of the lung tumor, the choice of treatment technique should be considered when treating the Cyberknife. The 4DCT images of 18 lung cancer patients were analyzed, and location error values were extracted through application program. The evaluation result was lower than the average position error only in the upper and the inner. These results suggest that the Vertebral tracking technique is effective when it is close to the pulmonary attachment or near the vertebral body, and the Synchrony technique is effective at other positions. In the future, we would like to study cyber knife treatment technique according to the position of the tumor as well as the volume of the lung and the respiratory cycle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.204-211
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• 2020

In this paper, we propose an optimal deep learning network architecture for main object location detection through weak supervised learning. The proposed network adds convolution blocks for improving the localization accuracy of the main object through weakly-supervised learning. The additional deep learning network consists of five additional blocks that add a composite product layer based on VGG-16. And the proposed network was trained by the method of weakly-supervised learning that does not require real location information for objects. In addition, Grad-CAM to compensate for the weakness of GAP in CAM, which is one of weak supervised learning methods, was used. The proposed network was tested through the CUB-200-2011 data set, we could obtain 50.13% in top-1 localization error. Also, the proposed network shows higher accuracy in detecting the main object than the existing method.

• Journal of the Korea Society for Simulation
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• v.30 no.4
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• pp.21-29
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• 2021

Inverse synthetic aperture radar (ISAR) image is a powerful tool to show the major scattering regions (scatterer points) on the target. It is normally used to identify and classify targets. Finding information for the scatter points of ISAR image plays an important role in modeling the features of targets. In this paper, we propose a modeling method to extract some information about the scatterer points by minimizing approximating error. Here, the extracted information include not only the location of scatterer points but also some statistical data about the error of the their location. These extracted data can be used to implement the randomness of the location of the scatterer points. Furthermore, we reconstruct an image from the extracted data for scatterer points obtained by our proposed method. And we show that the reconstructed ISAR image is well approximated to the original ISAR image in order to justify our proposed modeling method.