• Journal of the Korean Society for Precision Engineering
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• v.28 no.10
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• pp.1174-1180
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• 2011

Not all line or point features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity because it is difficult to determine the correspondence of line or point features with previously registered feature. Confused line and point features in cluttered environments leads to poor SLAM performance. We introduce a sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The reliable line feature is expressed by its end points and engaged togather in EKF SLAM to overcome the geometric limits and maintain the map consistency. Experimental results demonstrate the validity and robustness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2094-2102
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• 2016

In this paper, we propose an extended Kalman filter(EKF)-based simultaneous localization and mapping(SLAM) method using laser corner pattern matching for mobile robots. SLAM is one of the most important problems of mobile robot. However, existing method has the disadvantage of increasing the computation time, depending on the number of landmarks. To improve computation time, we produce the corner pattern using classified and detected corner points. After producing the corner patterns, it is estimated that mobile robot's global position by matching them. The estimated position is used as measurement model in the EKF. To evaluated proposed method, we preformed the experiments in the indoor environments. Experimental results of proposed method are shown to maintain an accuracy and decrease the computation time.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.10
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• pp.367-375
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• 1983

An extended Kalman filter (EKF) algorithm for estimating aerodynamic parameters from missile flight data is evaluated using simulated test data. The algorithm includes a general purpose 6-DOF missile airframe suitable for representing a variety of missile configurations. The EKF is demonstrated to be well suited as a postflight analysis tool for extracting large numbers of airframe parameters from flight test measurements. A structure identification algorithm is evaluated using synthetic measurement data. This algorithm used in conjunction with the parameter identification algorithm, can select that model from a family of candidate models which most likely produced the synthetic measurement data.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.171-175
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• 1998

This paper proposes a speed sensorless vector control of induction motor using a minimum-order EKF(extended kalman filter). Minimum-order EKF has the advantage of reducing the computational estimation cost because the stator current is not estimated. EKF does not deteriorate the performance of the overall speed control system, even though the measurements are relatively noisy. The estimated rotor speed is used for vector control and overall speed control. Computer simulations of the speed sensorless vector control are carried out to test the usefulness of the minimum-order EKF algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.155-156
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• 2009

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.706-712
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• 2014

The inverters used to drive Brushless DC motors (BLDC) include switching devices such as FETs and the faults in FETs cause severe performance degradation in systems where a BLDC acts as actuator. This paper presents a fault detection and isolation method for the FETs of an inverter for BLDC motor control systems, which is based on the EKF (Extended Kalman filter). Firstly, an equivalent circuit model for a BLDC motor plus its inverter system was derived. Secondly, a state-space equation was established, where the on-resistance of the FETs is expressed as a state variable and the EKF equation estimates the on-resistance. If the estimated resistance differs greatly from the known value, it can be asserted that there is a fault on that FET. Thirdly, the local convergence of the established EKF was proved. Finally, through the experiments, the performance of the proposed method was verified. The results show that the on-resistance is estimated close to the value specified in the FET data sheet in normal operation, whereas the estimated resistance is a much larger value than the normal one in case an FET fault occurs. Therefore, it is confirmed that the proposed fault detection and isolation method works appropriately in real systems.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.1
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• pp.55-60
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• 2011

The most basic algorithm in SLAM(Simultaneous Localization And Mapping) technique of mobile robots is EKF(Extended Kalman Filter) SLAM. However, it requires prior information of characteristics of the system and the noise model which cannot be estimated in accurate. By this limit, Kalman Filter shows the following behaviors in a highly uncertain environment: becomes too sensitive to internal parameters, mathematical consistency is not kept, or yields a wrong estimation result. In contrast, $H_{\infty}$ filter does not requires a prior information in detail. Thus, based on a idea that $H_{\infty}$ filter based SLAM will be more robust than the EKF-SLAM, we propose a framework of $H_{\infty}$ filter based SLAM and show that suggested algorithm shows slightly better result man me EKF-SLAM in a highly uncertain environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.463-468
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• 2010

The mobile robot which accomplishes a work in explored region does not know location information of surroundings. Traditionally, simultaneous localization and mapping(SLAM) algorithms solve the localization and mapping problem in explored regions. Among the several SLAM algorithms, the EKF (Extended Kalman Filter) based SLAM is the scheme most widely used. The EKF is the optimal sensor fusion method which has been used for a long time. The odometeric error caused by an encoder can be compensated by an EKF, which fuses different types of sensor data with weights proportional to the uncertainty of each sensor. In many cases the EKF based SLAM requires artificially installed features, which causes difficulty in actual implementation. Moreover, the computational complexity involved in an EKF increases as the number of features increases. And SLAM is a weak point of long operation time. Therefore, this paper presents a symmetrical model based SLAM algorithm(called M-SLAM).

• Smart Structures and Systems
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• v.31 no.2
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• pp.131-140
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• 2023

As well-known, the extended Kalman filter (EKF) is a powerful tool for parameter identification with limited measurements. However, traditional EKF is not applicable when the external excitation is unknown. By using least-squares estimation (LSE) for force identification, an EKF with unknown input (EKF-UI) approach was recently proposed by the authors. In this approach, to ensure the influence matrix be of full column rank, the sensors have to be deployed at all the degrees-of-freedom (DOFs) corresponding to the unknown excitation, saying collocated measurements are required. However, it is not easy to guarantee that the sensors can be installed at all these locations. To circumvent this limitation, based on the idea of first-order-holder discretization (FOHD), an improved EKF with unknown input (IEKF-UI) approach is proposed in this study for the simultaneous identification of structural parameters and unknown excitation. By using projection matrix, an improved observation equation is obtained. Few displacement measurements are fused into the observation equation to avoid the so-called low-frequency drift. To avoid the ill-conditioning problem for force identification without collocated measurements, the idea of FOHD is employed. The recursive solution of the structural states and unknown loads is then analytically derived. The effectiveness of the proposed approach is validated via several numerical examples. Results show that the proposed approach is capable of satisfactorily identifying the parameters of linear and nonlinear structures and the unknown excitation applied to them.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.225-234
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• 2009

Real-time Aerial Monitoring System (RAMS) is to perform the rapid mapping in an emergency situation so that the geoinformation such as orthophoto and/or Digital Elevation Model is constructed in near real time. In this system, the GPS/INS plays an very important role in providing the position as well as the attitude information. Therefore, in this study, the performance of an IMU sensor which is supposed to be installed on board the RAMS is evaluated. And the integration algorithm of GPS/INS are tested with simulated dataset to find out which is more appropriate in real time mapping. According to the static and kinematic results, the sensor shows the position error of 3$\sim$4m and 2$\sim$3m, respectively. Also, it was verified that the sensor performs better on the attitude when the magnetic field sensor are used in the Aerospace mode. In the comparison of EKF and UKF, the overall performances shows not much differences in straight as well as in curved trajectory. However, the calculation time in EKF was appeared about 25 times faster than that of UKF, thus EKF seems to be the better selection in RAMS.