• Journal of the Korea Institute of Military Science and Technology
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• v.6 no.3
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• pp.15-21
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• 2003

This paper contains a precise calibration technique for the position of seabed acoustic target and theoretical error analysis of calibration results. The target is deployed on seabed as a standalone transponder. The purpose of target is performing accuracy test for active sonar as well as position calibration itself. For the position calibration, relative range between target and test vessel should be measured using target's transponder function. The relative range data combined with vessel position can be converted into a estimated position of target by the application of nonlinear LSE method. The error analysis of position calibration was divided into two stages. One is for relative range estimator and the other for target position estimator. Numerical simulations for position calibration showed good matching between results and developed CRLB.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1205-1211
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• 2008

This paper describes methods to estimate 3-D position of target with respect to reference frame through monocular image from unmanned aerial vehicle (UAV). 3-D position of target is used as information for surveillance, recognition and attack. In this paper. 3-D position of target is estimated to make guidance and control law, which can follow target, user interested. It is necessary that position of target is measured in image to solve 3-D position of target. In this paper, kalman filter is used to track and output position of target in image. Estimation of target's 3-D position is possible using result of image tracking and information of UAV and camera. To estimate this, two algorithms are used. One is methode from arithmetic derivation of dynamics between UAV, carmer, and target. The other is LPV (Linear Parametric Varying). These methods have been run on simulation, and compared in this paper.

• Aerospace Engineering and Technology
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• v.6 no.1
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• pp.237-244
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• 2007

Estimation of target position is one of the main functions of surveillance UAVs, and is being used to various purposes but generally noisy target position is estimated due to the existence of random measurement errors. In this report, a method of diminishing target position estimation error by calculating target position using Kalman Filtered optimum values such as position, attitude of UAV and sight vector of optical instrument, is proposed.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.299-303
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• 2003

Position decision system embody to interpret for observation target position. Observation target is observed at observation post of 3 places. CCD cameras of observation post is achieved turning control by stepping motor. Controller interpret observation target's position to use direction and angle information of observation post. Controller and observation post used PIC16F877. PIC16F877 achieves rotation control of stepping motor and distance arithmetic of observation target. Result that measure this system 50 times was achieved correct position interpretation of 47 times. Position interpretation failure of 3 times was construed for cause in used controller special quality.

• Journal of Communications and Networks
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• v.15 no.2
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• pp.164-172
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• 2013

We consider the problem of secure and robust clustering for quantized target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose a new method for jointly activating the best group of candidate sensors that participate in data aggregation, detecting the malicious sensors and estimating the target position. Firstly, we select the appropriate group in order to balance the energy dissipation and to provide the required data of the target in the WSN. This selection is also based on the transmission power between a sensor node and a cluster head. Secondly, we detect the malicious sensor nodes based on the information relevance of their measurements. Then, we estimate the target position using quantized variational filtering (QVF) algorithm. The selection of the candidate sensors group is based on multi-criteria function, which is computed by using the predicted target position provided by the QVF algorithm, while the malicious sensor nodes detection is based on Kullback-Leibler distance between the current target position distribution and the predicted sensor observation. The performance of the proposed method is validated by simulation results in target tracking for WSN.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.441-448
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• 2020

GMTI (Ground Moving Target Indication) radar system can detect ground moving targets and can provide position and velocity information of each target. However, the azimuth position of target has some offset because of the hardware errors such as mechanical tolerances. In this case, an error occurs no matter how accurate the monopulse ratio is. In this paper, target position correction method in azimuth direction has been proposed. The received sum and difference signals of monopulse GMTI system are post-processed to correct the target azimuth angle error. This method is simple and adaptive for nonhomogeneous area because it can be implemented by using only software without any hardware modification or addition.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2004.11a
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• pp.132-135
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• 2004

In this study, we investigated the effect of threshold on a delivered dose in organ with internal motion by respiration. With mathematic model for 3D dose calculation reported by Lujan et al., we had calculated the position of organ as a function of time in previous study. This result presented that the variation of organ is within 2 mm from initial exhale position to the organ position during operating 1 s. Gating threshold, in this study, is determined to the moving region of target during 1s at a primary position of exhale. This period of gating threshold is 50% of the duty cycle in a half breathing cycle which is period from the top position of exhalation to the bottom position of inhalation. Radiation fields were then delivered under three conditions; 1) existent of moving target in the region of threshold(1sec, 1.5sec), 2) existent of moving target out of the region of threshold, 3) non-moving target. The non-moving target delivery represents a dose different induced due to internal organ motion.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.445-450
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• 2015

In the moving target problem, the velocity information of the moving target is very important as well as the high accuracy position information. To solve this problem, active researches are being conducted recently with combine the Time Difference of Arrival (TDOA) and Frequency Delay of Arrival(FDOA) measurements. However, since the FDOA measurement is utilizing the Doppler effect due to the relative velocity between the target source and the receiver sensor, it may be difficult to use the FDOA measurement if the moving target speed is not sufficiently fast. In this paper, we propose a method for estimating the position and the velocities of the target by using only the TDOA measurements for the low speed moving target in the indoor environment with sensor network. First, the target position and heading angle are obtained from the estimated positions of two attached transmitters on the target. Then, the target angular and linear velocities are also estimated. In addtion, we apply the Instrumental Variable (IV) technique to compensate the estimation error of the estimated target velocity. In simulation, the performance of the proposed algorithm is verified.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.4
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• pp.422-429
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• 2014

Proximity sensor networks are aimed at estimation kinematic state of target using estimated position of the target by each sensor node or target parameter. To analyze the kinematic state of target, traditional approaches require detections on multiple sensors, very large number of sensors to achieve acceptable performance. In this paper, we propose a novel method which can estimate predicted position of the underwater target using minimum proximity sensor with bearing information to this problem. The proposed algorithm was verified performance through simulation.

• Progress in Medical Physics
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• v.7 no.2
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• pp.67-77
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• 1996

Purpose: To get a 3-D coordinates of intracranial target position was investicated in axial, sagittal and coronal magnetic resonance imaging with a preliminary experimented target localizer. Material and methods : In preliminal experiments, the localizer is made of engineering plastic to avoid the distrubance of magnetic field during the MR image scan. The MR localizer displayed the 9 points in three different axial tomogram. The bright signal of localizer was obtjained from 0.1～0.3％ of paramagnetic gadolinium/DTPA solution in T1WI or T2WI. In this study, the 3-D position of virtual targets were examined from three different axial MR images and the streotactic position was compared to that of BRW stereotactic system in CT scan with same targets. Results: This study provided the actual target position could be obtained from single scan with MRI localizer which has inverse N-typed 9 bars. This experiment was accomplished with shimming test for detection of image distortion in MR image. However we have not found the image distortion in axial scan. The maximum error of target positions showed 1.0 mm in axial, 1.3 mm for sagittal and 1.7 mm for coronal image, respectivelly. The target localization in MR localizer was investicated with spherical virtual target in skull cadaver. Furthermore, the target position was confirmed with CRW stereotactic system showed a 1.3 mm in discrepancy. Summary : The intracranial target position was determined within 1.7 mm of discrepancy with designed MR localizer. We found the target position from axial image has more small discrepancy than that of sagittal and coronal image.