• Proceedings of the KIPE Conference
• /
• 2013.11a
• /
• pp.222-223
• /
• 2013

본 논문에서는 배터리 SOC 추정 정확도 향상을 위해 기존 EKF 추정 기법에 MMAE 방법을 접목시키는 방법을 제안한다. 노이즈의 세기에 따라 EKF 출력에 비중을 부여함으로써 배터리 사용 전 영역에서 SOC 추정 오차 저감이 가능하며, Matlab 시뮬레이션을 통하여 MMAE-EKF 알고리즘의 타당성을 검증하였다.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 2001.04a
• /
• pp.180-189
• /
• 2001

In this study, recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2001.10a
• /
• pp.117-126
• /
• 2001

In this study, the recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.57 no.2
• /
• pp.115-120
• /
• 2008

The EKF(Extended Kalman filter) method which is the state estimation algorithm of nonlinear stochastic system depends on the initial error and the estimated states. Therefore, the divergence of the estimated state can be caused if the initial values of the estimated states are not chosen as approximate real state values. In this paper, the demerit of the existing EKF method is improved using the EKF algorithm transformated by STWS(Single Term Walsh Series). This method linearizes each sampling interval of continous-time system through the derivation of an algebraic iterative equation without discretizing continuous system by the characteristic of STWS, the convergence of the estimated states can be improved. The validity of the proposed method is checked through comparison with the existing EKF method in simulation.

• Journal of KIISE:Software and Applications
• /
• v.30 no.7_8
• /
• pp.744-751
• /
• 2003

A hybrid least square Support Vector Machine combined with First Principle(FP) knowledge is proposed. We compare hybrid least square Support Vector Machine(HLS-SVM) with early proposed models such as Hybrid Neural Network(HNN) and HNN with Extended Kalman Filter(HNN-EKF). In the training and validation stage HLS-SVM shows similar performance with HNN-EKF but better than HNN, whereas, in the testing stage, it shows three times better than HNN-EKF, hundred times better than HNN model.

• Smart Media Journal
• /
• v.13 no.5
• /
• pp.9-18
• /
• 2024

Multi-Object trajectory modeling is a major challenge in MOT. CenterTrack tried to solve this problem with a Heatmap-based method that tracks the object center position. However, it showed limited performance when tracking objects with complex movements and nonlinearities. Considering the degradation factor of CenterTrack as the dynamic movement of pedestrians, we integrated the EKF into CenterTrack. To demonstrate the superiority of our proposed method, we applied the existing KF and UKF to CenterTrack and compared and evaluated it on various datasets. The experimental results confirmed that when EKF was integrated into CenterTrack, it achieved 73.7% MOTA, making it the most suitable filter for CenterTrack.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.40 no.1
• /
• pp.23-34
• /
• 2012

Space launch vehicles are designed to fly according to the elaborate pre-determined path. However, if a vehicle went out of the planned trajectory or its thrust terminated abnormally, or if a free-fall atmospheric reentry vehicle tracked by a tracking sensor became impossible to be measured, it is required to attempt to track by a another track equipment or estimate its impact point rapidly. In this paper a new algorithm is proposed, named the ITS-EKF combined with the Integrated Track Splitting (ITS) algorithm and the Extended Kalman Filter (EKF) to obtain the location information of a ballistic projectile without thrust, create its track and maintain it in an environment with clutter. For the reentry vehicle, the track performance is to be verified and the impact point is estimated by applying the simulation through ITS-EKF algorithm. To ensure the proposed algorithm's adequacy, by comparing the track performance and impact point distribution by the ITS-EKF with those of ITS-PF combined with ITS and Particle Filter (PF), it is confirmed that the ITS-EKF algorithm can be used an effective real-time On-line impact point prediction.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.37 no.4
• /
• pp.447-459
• /
• 2013

Many problems need to be solved before vision systems can actually be applied in industry, such as the precision of the kinematics model of the robot control algorithm based on visual information, active compensation of the camera's focal length and orientation during the movement of the robot, and understanding the mapping of the physical 3-D space into 2-D camera coordinates. An algorithm is proposed to enable robot to move actively even if the relative positions between the camera and the robot is unknown. To solve the correction problem, this study proposes vision system model with six camera parameters. To develop the robot vision control algorithm, the N-R and EKF methods are applied to the vision system model. Finally, the position accuracy and processing time of the two algorithms developed based based on the EKF and the N-R methods are compared experimentally by making the robot perform slender bar placement task.

• Journal of Positioning, Navigation, and Timing
• /
• v.6 no.3
• /
• pp.105-116
• /
• 2017

Several wireless localization algorithms are evaluated for the IR-UWB-based indoor location with the assumption that the ranging measurements contain the channelwise Non-Calibration Error (NCE). The localization algorithms can be divided into the Model-free Localization (MfL) methods and Model-based Kalman Filtering (MbKF). The algorithms covered in this paper include Iterative Least Squares (ILS), Direct Solution (DS), Difference of Squared Ranging Measurements (DSRM), and ILS-Common (ILS-C) methods for the MfL methods, and Extended Kalman Filter (EKF), EKF-Each Channel (EKF-EC), EKF-C, Cubature Kalman Filter (CKF), and CKF-C for the MbKF. Experimental results show that the DSRM method has better accuracy than the other MfL methods. Also, it demands smallest computation time. On the other hand, the EKF-C and CKF-C require some more computation time than the DSRM method. The accuracy of the EKF-C and CKF-C is, however, best among the 9 methods. When comparing the EKF-C and CKF-C, the CKF-C can be easily used. Finally, it is concluded that the CKF-C can be widely used because of its ease of use as well as it accuracy.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.16 no.3
• /
• pp.266-273
• /
• 2011

This paper proposes a novel sensorless control scheme for a Permanent Magnet Synchronous Motor (PMSM) by using a parallel reduced-order Extended Kalman Filter. The proposed scheme can obtain rotor position and speed by back-EMF that is estimated by reduced-order EKF and save computation time greatly due to using a parallel structure that works by turns every sampling time. Therefore, proposed scheme has merits of conventional EKF, and problems of parameter sensitivity are partially overcome. And proposed scheme can safely estimate rotor speed and position by using new algorithms according to driving regions. Experimental results show the validity of the proposed estimation technique, and to verify the merit of the proposed scheme, a comparison of a new reduced-order EKF algorithm with a conventional EKF algorithm has been also made in terms of computation time.