• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.53-54
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• 2015

The maritime industry is expanding at an alarming rate and as such there is a perpetual need to improve situation awareness in the maritime environment using new and emerging technology. Tracking is one of the numerous ways of enhancing situation awareness by providing information that may be useful to the operator. The tracking system described herein comprises determining existing states of own ship, state prediction and state compensation caused by random noise. The purpose of this paper is to analyze the process of tracking and develop a tracking algorithm by using ${\alpha}-{\beta}-{\gamma}$ tracking filter under a random noise or irregular motion for use in a warship. The algorithm involves initializing the input parameters of position, velocity and course. The actual positions are then computed for each time interval. In addition, a weighted difference of the observed and predicted position at the nth observation is added to the predicted position to obtain the smoothed position. This estimation is subsequently employed to determine the predicted position at (n+1). The smoothed values, predicted values and the observed values are used to compute the twice distance root mean square (2drms) error as a measure of accuracy of the tracking module.

• Journal of Navigation and Port Research
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• v.37 no.5
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• pp.447-452
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• 2013

Vessel traffic system uses multiple sea surveillance radars as a primary sensor to obtain maritime traffic information like as ship's position, speed, course. The systematic errors such as the range bias and the azimuth bias of the two-dimensional radar system can significantly degrade the accuracy of the radar image and target tracking information. Therefore, the systematic errors of the radar system should be corrected precisely in order to provide the accurate target information in the vessel traffic system. In this paper, it is proposed that the method compensates the range bias and the azimuth bias using AtoN information installed at VTS coverage. The radar measurement residual error model is derived from the standard error model of two-dimensional radar measurements and the position information of AtoN, and then the linear Kalman filter is designed for estimation of the systematic errors of the radar system. The proposed method is validated via Monte-Carlo runs. Also, the convergence characteristics of the designed filter and the accuracy of the systematic error estimates according to the number of AtoN information are analyzed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1448-1454
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• 2018

In an optical observing system, three pairs of observations at equal time intervals are required for the orbit determination method to obtain orbital information of an unknown space objects. In this paper, we propose a method of estimating a streak for acquiring three pairs of observations using one streak image information. Satellite trajectory simulation data were generated for nine cases using the STK program in order to verify the characteristics of the orbit of space object and estimation performance. Simulation was performed by applying three approaches that can estimate the next streak position after a few seconds from one streak image information, and the estimation performance was evaluated. Linear vector method and Kalman Filter method based on the linear assumption tend to increase the estimation error in the region where the nonlinearity is large. However estimation method using the polynomial curve fitting based on the least square method showed smaller and uniform error result than the previous methods.

• 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 the Society of Naval Architects of Korea
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• v.30 no.4
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• pp.8-16
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• 1993

Auto-Pilot System uses heading angle information via the position sensor and the rudder device to control the ship direction. Most of the control logics are composed of the state estimation and control algorithms assuming that the measurement device and the actuator have no fault except the measurement noise. But such asumptions could bring the danger in real situation. For example, if the heading angle measuring device is out of order the control action based on those false position information could bring serious safety problem. In this study, the control system including improved method for processing the position information is applied to the Auto-Pilot System. To show the difference between general state estimator and F.D.F., BJDFs for the sensor and the actuator failure detection are designed and the performance are tested. And it is shown that bias error in sensor could be detected by state-augmented estimator. So the residual confined in the 2-dim in the presence of the sensor failure could be unidirectional in output space and bias sensor error is much easier to be detected.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.2
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• pp.111-116
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• 2014

In this paper, we estimates the direction of arrival of desired a target using linear array antenna in wireless communication. Direction of arrival estimation is to estimate for desired target position among incident signals on receiver array antennas. This paper improved estimation of direction of arrival for target using optimum weight, high resolution adaptive beamforming algorithm, and sparse matrix for driection of arrival estimation. Through simulation, we showed that we are performance the analysis to compare general algorithm with proposed algorithm. We show that propose algorithm more improve for direction of estimation than general beamforming algorithm.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1865-1873
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• 2017

The feature-based visual SLAM requires 3D positions for the extracted feature points to perform 3D-2D motion estimation. LiDAR can provide reliable and accurate 3D position information with low computational burden, while stereo camera has the problem of the impossibility of stereo matching in simple texture image region, the inaccuracy in depth value due to error contained in intrinsic and extrinsic camera parameter, and the limited number of depth value restricted by permissible stereo disparity. However, the sparsity of LiDAR data may increase the inaccuracy of motion estimation and can even lead to the result of motion estimation failure. Therefore, in this paper, we propose three interpolation methods which can be applied to interpolate sparse LiDAR data. Simulation results obtained by applying these three methods to a visual odometry algorithm demonstrates that the selective bilinear interpolation shows better performance in the view point of computation speed and accuracy.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.10
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• pp.569-574
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• 2003

Computer vision system is broadly adapted like as autonomous vehicle system, product line inspection, etc., because it has merits which can deal with environment flexibly. However, for applying it for that industry, it has to clear the problem that recognize position parameter of itself. So that computer vision system stands in need of camera calibration to solve that. Camera calibration consists of the intrinsic parameter which describe electrical and optical characteristics and the extrinsic parameter which express the pose and the position of camera. And these parameters have to be reorganized as the environment changes. In traditional methods, however, camera calibration was achieved at off-line condition so that estimation of parameters is in need again. In this paper, we propose a method to the calibration of camera using line correspondence in image sequence varied environment. This method complements the corresponding errors of the point corresponding method statistically by the extraction of line. The line corresponding method is strong by varying environment. Experimental results show that the error of parameter estimated is within 1% and those is effective.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.337-340
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• 2011

In location finding system using spaced multi-sensor, Depending on the signal source's location and the location of the sensors Position estimation accuracy is determined. This phenomenon is called GDOP effect. and to minimize these effects, research is needed on how. In this paper, I will describe how to minimize GDOP effect, estimating GDOP using angle of arrivals of multi sensors, and removing sensor error factor.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.111-116
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• 2008

Radar system must estimate exactly quickness and target in interference channel. Because interference of radio channel is multipath channel by artificial structure and nature structure. signal estimation is difficult. As long as, get rid of interference signal have been study digital beamforming, adaptive array antenna and so on. In this paper, proposed SPT-SALCMV beamforming algorithm get rid of coherent interference algorithm and adaptive array antenna. Adaptive array forms null pattern and reduces gains for direction of interference signal. And estimate signal that want by keeping gains of beam pattern changelessly to target signal direction. In this paper, proposed SPT-SALCMV algorithm was exactly received position of target. But general SPT-LCMV algorithm resulted beam error about 30degrees. Therefore, proved that SPT-SALCMV algerian that propose in this paper is more excellent than genaral SPT-LCMV algorithm.