• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.1
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• pp.51-59
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• 2013

This paper researches location estimating method in CDMA system. Previously proposed positioning algorithms are difficult to estimate accurate position in indoor environments, and possible to limited position. This paper proposes enhanced algorithm using received PN pilot signals from base stations to enhance previous algorithms. For estimating position, we set the threshold value and use over the threshold value in received signals. After selecting signals, we estimate position using TDOA algorithm. And the cases which TDOA algorithm cannot use to estimate position, we use Pattern Matching algorithm. The proposed method system showed the improved performance in estimating parameters and locating positions by computer simulations.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.4
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• pp.183-192
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• 2019

In fingerprint-based wireless positioning, it is necessary to establish a DB of the unmeasured area. To this end, a method of estimating the position of a base station based on a signal propagation model, and a method of estimating the information of the received signal in the unmeasured area based on the estimated position of the base station have been investigating. The purpose of this paper is to estimate the position of the base station using the measured information and to analyze the performance of the positioning. Vehicles equipped with a GPS receiver and signal measuring equipment travel the service area and acquire location-based Reference Signal Received Power (RSRP) measurements. We propose a method of estimating the position of the base station using the measured information. And the performance of the proposed method is analyzed on a simulation basis. The simulation results confirm that the accuracy of the positioning is affected by the measured area and the Dilution of Precision (DOP), the accuracy of the position information obtained by the GPS receiver, and the errors of the signal included in the RSRP. Based on the results of this paper, we can expect that the position of the base station can be estimated and the DB of the unmeasured area can be constructed based on the estimated position of the base stations and the signal propagation model.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.4
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• pp.341-348
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• 2013

A location estimation algorithm based on the sea-surface beacon is proposed in this paper. The beacon is utilized to provide ultrasonic signals to the underwater vehicles around the beacon to estimate precise position of underwater vehicles (ROV, AUV, Diver robot), which is named as USBL (Ultra Short Baseline) system. It utilizes GPS and INS data for estimating its position and adopts DWT (Discrete Wavelet Transform) de-noising filter and UKF (Unscented KALMAN Filter) elaborating the position estimation. The beacon system aims at estimating the precise position of underwater vehicle by using USBL to receive the tracking signals. The most important one for the precise position estimation of underwater vehicle is estimating the position of the beacon system precisely. Since the beacon is on the sea-waves, the received GPS signals are noisy and unstable most of times. Therefore, the INS data (gyroscope sensor, accelerometer, magnetic compass) are obtained at the beacon on the sea-surface to compensate for the inaccuracy of the GPS data. The noises in the acceleration data from INS data are reduced by using DWT de-noising filter in this research. Finally the UKF localization system is proposed in this paper and the system performance is verified by real experiments.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.4
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• pp.64-71
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• 1997

Objective of calibration is to find out the accurate kinematic relationships between robot joint angles and the position of the end-effector by estimating accurate model parameters defining the kinematic function. Estimating the model parameters requires measurement of the end-effector position at a number of different robot configurations. This paper studies the implication of measurement configurations in robot calibration. For selecting appropriate measurement configurations in robot calibration, an index is defined to measure the observability of the model parameters with respect to a set of robot configurations. It is found that, as the observability index of the selected measurement configurations increase the attribution of the position errors to the parameter errors becomes dominant while the effects of the measurement and unmodeled errors are less significant; consequently better estimation of parameter errors is expected. To demonstrate the implication of the observability measure in robot calibration, computer simulations are performed and their results are discussed.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.11
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• pp.884-890
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• 2018

In this paper, an improved method of estimating static source location is proposed based on the direct position determination(DPD) method, which estimates a source position directly using received signals. When the source position is estimated using the conventional DPD method, the estimation accuracy and error depend on a pair of receivers: a reference receiver and one of the multiple moving receivers. Based on this, the weighting values of the estimating source location were obtained using the covariance matrix for the pair of receivers($S_1$, $S_{2i}$) and applied to the DPD algorithm. Finally, the source position was estimated using the proposed DPD algorithm, and it was verified that the estimation accuracy improved, compared to the conventional DPD algorithm.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.2
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• pp.169-176
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• 2012

GPS(Global Positioning System) is widely used for the position estimation of an aerial vehicle. However, GPS may not be available due to hostile jamming or strategic reasons. A vision-based position estimation method can be effective if GPS does not work properly. In mountainous areas without any man-made landmark, a horizon is a good feature for estimating the position of an aerial vehicle. In this paper, we present a new method to estimate the position of the aerial vehicle equipped with a forward-looking infrared camera. It is assumed that INS(Inertial Navigation System) provides the attitudes of an aerial vehicle and a camera. The horizon extracted from an infrared image is compared with horizon models generated from DEM(Digital Elevation Map). Because of a narrow field of view of the camera, two images with a different camera view are utilized to estimate a position. The algorithm is tested using real infrared images acquired on the ground. The experimental results show that the method can be used for estimating the position of an aerial vehicle.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.688-693
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• 2007

The INS/GPS integration system is designed by employing an adaptive filter that can estimate the measurement noise variance using the residual of the filter. To verify the efficiency of the proposed loosely-coupled INS/GPS integration system, simulation is performed by assuming that GPS information has large position errors. Simulation results show that the proposed integration system with the adaptive filter is more effective in estimating the position and attitude errors than those with the Extended Kalman Filter.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.99-104
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• 2021

The technology of estimating a movement trajectory using a monocular camera such as a smartphone and composing a surrounding 3D image is key not only in indoor positioning but also in the metaverse service. The most important thing in this technique is to estimate the coordinates of the moving camera center. In this paper, a new algorithm for geometrically estimating the moving distance is proposed. The coordinates of the 3D object point are obtained from the first and second photos, and the movement distance vector is obtained using the matching feature points of the first and third photos. Then, while moving the coordinates of the origin of the third camera, a position where the 3D object point and the feature point of the third picture coincide is obtained. Its possibility and accuracy were verified by applying it to actual continuous image data.

• Wind and Structures
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• v.34 no.6
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• pp.469-482
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• 2022

The generalized extreme value distribution (GEVD) is frequently used to fit the block maximum of environmental parameters such as the annual maximum wind speed. There are several methods for estimating the parameters of the GEV distribution, including the least-squares method (LSM). However, the application of the LSM with the expected order statistics has not been reported. This study fills this gap by proposing a fitting method based on the expected order statistics. The study also proposes a plotting position to approximate the expected order statistics; the proposed plotting position depends on the distribution shape parameter. The use of this approximation for distribution fitting is carried out. Simulation analysis results indicate that the developed fitting procedure based on the expected order statistics or its approximation for GEVD is effective for estimating the distribution parameters and quantiles. The values of the probability plotting correlation coefficient that may be used to test the distributional hypothesis are calculated and presented. The developed fitting method is applied to extreme thunderstorm and non-thunderstorm winds for several major cities in Canada. Also, the implication of using the GEVD and Gumbel distribution to model the extreme wind speed on the structural reliability is presented and elaborated.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.328-333
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• 1996

In this paper, we propose a new method to estimate robot position without landmark. At first, it is studied to estimate robot state using Markov decision rule. And, a matching method is discussed for estimating current position more accurately under the estimated current state. At second, we combine or fuse the matching method with the POMDP method in order to estimate the position under a dynamically changing environment. Finally we will show that our method can estimate the position precisely and robustly of which error are not cumulated through simulation results.