• Journal of Magnetics
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• v.19 no.1
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• pp.90-100
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• 2014

Position estimation that uses only active phase voltage and current is presented, to perform high accuracy position sensorless control of a SRM drive. By extracting the amplitude of the first switching harmonic terms of phase voltage and current for a PWM period through Fast Fourier Transform (FFT), the flux-linkage and position are estimated without external hardware circuitry, such as a modulator and demodulator, which result in increased cost, as well as large position estimation error, produced when the motional back EMF is ignored near zero speed. A two-phase SRM drive system, consisting of an asymmetrical converter and a conventional closed-loop PI current controller, is utilized to validate the performance of the proposed position estimation scheme in comprehensive operating conditions. It is shown that the estimated values very closely track the actual values, in dynamic simulations and experiments.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.2
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• pp.208-213
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• 2012

In this paper, the radial error of a rotary table at five-axis machine tool is evaluated by utilizing ISO 230-2 and estimation method using double ball-bar. The geometric error of a rotary table is defined as position dependent geometric errors or position independent geometric errors according to their physical character. Then estimation method of geometric errors using double ball-bar is simply summarized including measurement path, parametric modeling and least squares approach. To estimate representative radial error, offset error, set-up error which affect to the double ball-bar data, mean value of measured data including CCW/CW-direction are used at estimation process. Radial errors are separated from measured data and used for evaluation with ISO 230-2. Finally, suggested evaluation method is applied to a rotary table at five-axis machine tool and its result is analyzed to improve the accuracy of the rotary table.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4408-4428
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• 2019

Due to the rapid advancement in smart phones, numerous new specifications are developed for variety of applications ranging from health monitoring to navigations and tracking. The word indoor navigation means location identification, however, where GPS signals are not available, accurate indoor localization is a challenging task due to variation in the received signals which directly affect distance estimation process. This paper proposes a hybrid approach which integrates fingerprinting based K-Nearest Neighbors (K-NN) and lateration based MinMax position estimation technique. The novel idea behind this hybrid approach is to use Euclidian distance formulation for distance estimates instead of indoor radio channel modeling which is used to convert the received signal to distance estimates. Due to unpredictable behavior of the received signal, modeling indoor environment for distance estimates is a challenging task which ultimately results in distance estimation error and hence affects position estimation process. Our proposed idea is indoor position estimation technique using Bluetooth enabled smart phones which is independent of the radio channels. Experimental results conclude that, our proposed hybrid approach performs better in terms of mean error compared to Trilateration, MinMax, K-NN, and existing Hybrid approach.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.1205-1211
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• 2004

We propose a train position estimation method for automatic train control system. The accurate train position should be continuously feedback to control system for safe and efficient operation of trains in railway. In this paper, we propose the sensor fusion method integrating the tachometer, the transponder, and the doppler sensor for estimation of train position. The external sensors(transponder, doppler sensor) are used to compensate for the error of internal sensor(tachometer). The Kalman filter is also applied to reduce the measurement error of the sensors. Simulation results are then presented to verify the usefulness of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.3
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• pp.401-407
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• 2019

In this paper, the position estimation considering wheel slip of mecanum wheeled mobile robots is discussed. Since the mecanum wheeled mobile robot does not need a space to rotate, it is very suitable in narrow industrial fields. However, the slip caused by the roller attached to the wheel makes it difficult to estimate the position precisely. Due to these limitations, mecanum wheels are rarely applied to unmanned mobile robots in automation factories. In this paper, a method to compensate the orientation and distance error caused by the slip is proposed. The exact orientation is measured by fusing gyro and magnetometer sensor data with application of Kalman filter. In addition, the kinematic model accounting slip effects will be defined to compensate the distance error.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.27-31
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• 2003

Underwater acoustic navigation systems are classified into three systems: ultra-short baseline (USBL), short baseline (SBL), and long baseline (LBL). Because the USBL system estimates the angle of a submersible, the estimation error becomes large if the submersible is far from the USBL transducer array mounted under a support vessel. SBL and LBL systems estimate submersible's location more accurately because they have wider distribution of measuring sensors. Especially LBL systems are widely used as a navigation system for deep ocean applications. Although it is most accurate system it still has estimation errors because of noise, measurement error, refraction and multi-path of acoustic signal, or wrong information of the distributed transponders. In this paper the estimation error of the LBL system are analyzed from a point of sensitivity. It is assumed that the error exists only in the distance between a submersible and the transponders. For this purpose sensitivity of the estimated position with respect to relative distances between them is analyzed. The result says that estimation error is small if the submersible is close to transponders but not near the ocean bottom.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.1
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• pp.118-128
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• 2017

In this study, we propose a real time inertial navigation system/global positioning system (INS/GPS) integrated smoothing algorithm based on an extended Kalman filter (EKF) and a position damping loop (PDL) for synthetic aperture radar (SAR). Integrated navigation algorithms usually induce discontinuities due to error correction update by the Kalman filter, which are as detrimental to the performance of SAR as the relative position error. The proposed smoothing algorithm suppresses these discontinuities and also reduces the relative position error in real time. An EKF estimates the navigation errors and sensor biases, and all the errors except for the position error are corrected directly and instantly. A PDL activated during SAR operation period imposes damping effects on the position error estimates, where the estimated position error is corrected smoothly and gradually, which contributes to the real time smoothing and small relative position errors. The residual errors are re-estimated by the EKF to maintain the estimation performance and the stability of the overall loop. The performance improvements were confirmed by Monte Carlo simulations. The simulation results showed that the discontinuities were reduced by 99.8% and the relative position error by 48% compared with a conventional EKF without a smoothing loop, thereby satisfying the basic performance requirements for SAR operation. The proposed algorithm may be applicable to low cost SAR systems which use a conventional INS/GPS without changing their hardware configurations.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.178-185
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• 2011

In this paper, we propose the position estimation algorithm based on the multisensor fusion using equalization of state variables and feedback structure. First, the state variables measured from INS of main sensor with large error and DVL of assistance sensor with small error are measured before prediction phase. Next, the equalized state variables are entered to each filter and fused the enhanced state variables for prediction and update phases. Finally, the fused state variables are returned to the main sensor for improving the position estimation of UUV. For evaluation, we create the moving course of UUV by simulation and confirm the performance of position estimation by applying the proposed algorithm. The evaluation results show that the proposed algorithm is the best for position estimation and also possible for robust position estimation at the change period of moving courses.

• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.104-108
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• 2003

This paper explained algorithm for a initial pole position estimation of a permanent magnet linear synchronous motor(PMLSM). Generally this motor is considered initial pole position with a position sensor such as incremental encoder for the precise initial pole position estimation and high performance. But this is based on the principle that the initial pole position is accomplished by the PI controller using the maximum values of a position error generated by the new proposed two reference frames and also by using a rated force for input. the proposed algorithm does not utilize the general methods such as impedance ratio, EMF and using the magnetic saturation. In other words, this can be applied without respect to variety of the motor structure because of insensitivity to the motor parameters. In conclusion, simulation results are presented to confirm performance of initial pole position estimation method.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.1
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• pp.89-96
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• 2016

TDOA(time delay of arrival) position estimate from acoustic measurement of artillery shell impact is studied in order to develop a targeting evaluation system. Impact position is calculated from the intersections of hyperbolic estimates based on the least square Taylor series method. The mathematical process of Taylor series estimation is known to be robust. However, the concern lays with the accuracy because it is sensitive to the bias caused by the randomness of measurement situation. The measurement error typically occurs from the distortion of waveform, change of travelling path, and sensor position error. For outdoor measurement, a consideration should be made on the atmospheric condition such as temperature and wind which can possibly change the trajectories of rays of sound. It produces wrong propagation time events accordingly. Ray tracing and optimization techniques are introduced in this study to minimize the bias induced by the ray of sound. The numerical simulation shows that the atmospheric correction improves the estimation accuracy.