• Journal of the Korean Institute of Navigation
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• v.7 no.2
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• pp.1-45
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• 1983

This paper concerns the applications of the Kalman filter to navigation and the develment of computer programs of the navigational calculations. Methods to apply the Kalman filter to celestial fix, fix by cross bearing and cocked hat are proposed, and numerical simulations under various noise conditiions are conducted. The accuracy of the optimal positions obtained by the Kalman filter is compared with that of the fixed positiions by radial error method. In the case of celestial fix, an algorithm to estimate the optimal positions by using the linear Kalman filter is presented. The optimal positions by the Kalman filter are compared with the running fixes and with the most probable positions obtained from a single line of position. It is confirmed that the resutls of the proposed method are more accurate than the others. In practical piloting, bearings are generally measured intermittently and the measurement process is nonlinear. It is, therefore, difficult for us to apply the Kalman filter to fix by cross bearing. In order to be used in such an unfavorable case, the extended Kalman filter is revised and the aplicability of the revised extended Kalman filter is checked by numerical simulation under various noise conditions. In a cocked hat, an inside or outside fix is dependent only upon azimuth spread, if the error of each line of position is assumed to be equal both in magnitude and sign. A new technique of selecting a ship's position between an inside fix and an outside fix in a cocked hat by using fix determinant derived from the equation of three lines of position is also presented. The relations among the optimal position by Kalman filter, incentre (or excentre) and random error centtre of the cocked hat are discussed theoretically and the accuracy of the optimal position is compared with that of the others by numerical simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.1039-1044
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• 2000

In this paper, the error caused by the measurement delay in INS/GPS integrated systems with Kalman filtering is defined and analyzed through the analytical method and the simulation. It is proved that the error of measurement delay causes not only the position error but also the estimate error of the x-axis accelerometer bias when a vehicle turns. And the estimation method of the delay time and the compensation method using an extrapolation method are presented. The performance of the compensation method is shown by the analytic method and the simulation.

• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1436-1449
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• 2009

The value of sensing information is decided according to positions of sensor nodes, which are very important in sensor networks. In this paper, we propose a method that estimates positions of nodes by using adjacent node information and received signal strength in a sensor network. With the proposed method, we can find positions of nodes easily because we use information that nodes have. Moreover, we can find distribution easily for all the nodes because we can measure a relative position for a node whose position is not known based on anchor nodes whose positions are already known. We utilized Use case diagram, activity diagram and State machine diagram among several diagrams of UML to implement proposed method in sensor networks that is dynamic system. We can understand exact flow for each function of the proposed method in node position estimation system can be implemented easily. And we can be confirmed that the position of estimated nodes has a little error.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.2
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• pp.121-127
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• 2017

From the perspective of commercializing rotating machines equipped with magnetic bearings, maintaining the error between the mechanical center and the magnetic center within an acceptable level is crucial. The existing method of measuring the center error is to adjust the position references that minimize the current imbalance present in levitation control outputs. However, this method can be applied only after all the components of the system are operational. In this paper, we present a new method of estimating the center error by using only the position sensors and a current source. A force model that relates the position of the rotor with the coil currents is set up. Using this model, the center error is estimated by minimizing the difference between the force angles and the contact angles measured in a pull test. The feasibility of the method is numerically and experimentally validated.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.123-129
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• 2009

It is important to locate nodes in the research of wireless sensor network. In this paper, we propose a method that estimates the positions of nodes by using adjacent node information and signal strength in wireless sensor network. With this method, we can find positions of nodes easily because we use Information that nodes have. And we can make a map for all the nodes because we can measure a relative position for an node whose position is not known based on anchor nodes whose positions are already known. In addition, we can confirm whether nodes are placed appropriately. We confirmed that we can locate positions of unknown nodes with small error through verifying the proposed method.

• Journal of Power Electronics
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• v.10 no.5
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• pp.485-490
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• 2010

Sensorless control methods are generally used in motor control for home-appliances because of the material cost and manufactureing standard restrictions. The current model-based control algorithm is mainly used for PMSM sensorless control in the home-appliance industry. In this control method, the rotor position is estimated by using the d-axis and q-axis current errors between the real system and a motor model of the position estimator. As a result, the accuracy of the motor model parameters are critical in this control method. A mismatch of the PMSM parameters affects the speed and torque in low speed, steadystate responses. Rotor position errors are mainly caused by a mismatch of the stator resistance. In this paper, a stator resistance compensation algorithm is proposed to improve sensorless control performance. This algorithm is easy to implement and does not require a modification of the motor model or any special interruptions of the controller. The effectiveness of the proposed algorithm is verified through experimental results.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.11
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• pp.1139-1145
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• 2008

A robust position sensing system is proposed in this paper for a ubiquitous mobile robot which moves indoors as well as outdoors. The Differential GPS (DGPS) which has a position estimation error of less than 5 m is a general solution when the mobile robot is moving outdoor, while an active beacon system (ABS) with embedded ultrasonic sensors is reliable as an indoor positioning system. The switching from the outdoor to indoor or vice versa causes unstable measurements on account of the reference coordinates and algorithm changes. To minimize the switching time in the position estimation and to stabilize the measurement, a robust position sensing system is proposed. In the system, to minimize the switching delay, the door positions are stored and updated in a database. Using the database, the approaching status of the mobile robot from indoor to outdoor or vice versa has been checked and the switching conditions are prepared before the mobile robot actually moves out or moves into the door. The reliability and accuracy of the robust positioning system based on DGPS and ABS are verified and demonstrated through the real experiments using a mobile robot prepared for this research.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.54-61
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• 2012

In this paper, the pose estimation method for the satellite GTB (Ground Test Bed) using vision/MEMS IMU (Inertial Measurement Unit) integrated system is presented. The GTB for verifying a satellite system on the ground is similar to the mobile robot having thrusters and a reaction wheel as actuators and floating on the floor by compressed air. The EKF (Extended Kalman Filter) is also used for fusion of MEMS IMU and vision system that consists of a single camera and infrared LEDs that is ceiling landmarks. The fusion filter generally utilizes the position of feature points from the image as measurement. However, this method can cause position error due to the bias of MEMS IMU when the camera image is not obtained if the bias is not properly estimated through the filter. Therefore, it is proposed that the fusion method which uses the position of feature points and the velocity of the camera determined from optical flow of feature points. It is verified by experiments that the performance of the proposed method is robust to the bias of IMU compared to the method that uses only the position of feature points.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.621-624
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• 1999

This paper describes a distance estimation method of object's motion in soccer image sequence by tracking field features. And we quantitatively evaluate the estimation accuracy We suppose that the input image sequence is taken with a camera on static axis and includes only zooming and panning transformation between frames. Adaptive template matching is adopted for non-rigid object tracking. For background compensation, feature templates selected from reference frame image are matched in following frames and the matched feature point pairs are used in computing Affine motion parameters. A perspective displacement field model is used for estimating the real distance between two position on Input Image. To quantitatively evaluate the accuracy of the estimation, we synthesized a 3 dimensional virtual stadium with graphic tools and experimented on the synthesized 2 dimensional image sequences. The experiment shows that the average of the error between the actual moving distance and the estimated distance is 1.84%.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3
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• pp.49-54
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• 1999

In this paper, we propose a new method to estimate the initial DOA of a new appearing source in wideband multisource beamforming and tacking with array sensor position calibration algorithm. By using a beampattern formula for initial DOA detection, the proposed method keeps estimation error within possible tracking range and can be applied to several beamformers with different mainlobe width by adjusting DOA resolution. The simulation results show the performances of source detection and tracking.