• Journal of Hydrospace Technology
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• v.1 no.1
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• pp.66-74
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• 1995

Unknown parameters can be determined by system identification techniques. Extended Kalman filter method was introduced as a real time estimator of hydrodynamic derivatives but it has the problem named the coefficient drift. In this study, 2nd order filter estimates hydrodynamic derivatives in Abkowitz model In order to reduce the coefficient drift, parallel processing is used. The measured state and ship trajectory are compared with the estimated values. Parallel processing of 2nd order filter gives very similar results to parallel processing of extended Kalman filter. Parallel processing cannot not remove the coefficient drift perfectly, but it reduces the estimation error.

• Speech Sciences
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• v.11 no.4
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• pp.53-66
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• 2004

The nonlinear speech enhancement method with interactive parallel-extended Kalman filter is applied to speech contaminated by additive white noise. To represent the nonlinear and nonstationary nature of speech. we assume that speech is the output of a nonlinear prediction HMM (NPHMM) combining both neural network and HMM. The NPHMM is a nonlinear autoregressive process whose time-varying parameters are controlled by a hidden Markov chain. The simulation results shows that the proposed method offers better performance gains relative to the previous results [6] with slightly increased complexity.

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

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.5
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• pp.336-343
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• 2008

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-EKF that is estimated by reduced-order ETD and save computation time great)y 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.12
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• pp.1225-1231
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• 2009

One reaction wheel cluster of satellite usually has four reaction wheels. Each wheel is not arranged parallel to the attitude axis of satellite. Therefore, if one reaction wheel is broken, it is very hard to isolate the fault except using the sensors of wheel itself. In this paper, the isolator of satellite reaction wheel cluster is designed. Using a dual filter, FDP(Fault Detection Parameter) is made to detect fault, and using a multi-hypothesis extended Kalman filter, fault isolation of wheel cluster is done. We verify the improvement of isolation performance of wheel cluster by simulation with 4-reaction wheel cluster.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.26-29
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• 2001

In a target tracking problem the radar glint noise has non-Gaussian heavy-tailed distribution and will seriously affect the target tracking performance. In most nonlinear situations an Extended Robust Kalman Filter(ERKF) can yield acceptable performance as long as the noises are white Gaussian. However, an Extended Robust $H_{\infty}$ Filter (ERHF) can yield acceptable performance when the noises are Laplacian. In this paper, we use the Interacting Multiple Model(IMM) estimator for the problem of target tracking with glint noise. In the IMM method, two filters(ERKF and ERHF) are used in parallel to estimate the state. Computer simulations of a real target tracking shows that hybrid filter used the IMM algorithm has superior performance than a single type filter.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.448-449
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• 2010

This paper proposes a sensorless control strategy for the Interior Permanent Magnet Synchronous Motor (IPMSM) by using the parallel reduced-order Extended Kalman Filter. The sensorless control strategy is composed with two EKFs alternately computed every sampling period with a new model. The new model is based on the extended electromotive force (EEMF) which has a simple structure, making position estimation possible without approximation. The proposed strategy can save computation time and estimate rotor speed and position. To verify the merit of the proposed strategy, simulation and experimental results validate the theoretical analysis and show the feasibility of the proposed control strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4103-4117
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• 2014

Image processing and computer vision algorithms are gaining larger concern in a variety of application areas such as robotics and man-machine interaction. Vision allows the development of flexible, intelligent, and less intrusive approaches than most of the other sensor systems. In this work, we determine the location and orientation of a mobile robot which is crucial for performing its tasks. In order to be able to operate in real time there is a need to speed up different vision routines. Therefore, we present and evaluate a method for introducing parallelism into the multiple non-overlapping camera pose estimation algorithm proposed in [1]. In this algorithm the problem has been solved in real time using multiple non-overlapping cameras and the Extended Kalman Filter (EKF). Four cameras arranged in two back-to-back pairs are put on the platform of a moving robot. An important benefit of using multiple cameras for robot pose estimation is the capability of resolving vision uncertainties such as the bas-relief ambiguity. The proposed method is based on algorithmic skeletons for low, medium and high levels of parallelization. The analysis shows that the use of a multiprocessor system enhances the system performance by about 87%. In addition, the proposed design is scalable, which is necaccery in this application where the number of features changes repeatedly.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.25-36
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• 2004

This paper proposes and measures the performance of a distributed EIT (Electrical Impedance Tomography) image reconstruction algorithm which has a master-slave structure. The image construction is a computation based application of which the execute time is proportional to the cube of the unknowns. After receiving a specific frame from the master, each computing node extracts the basic elements by executing the first iteration of Kalman Filter in parallel. Then the master merges the basic element lists into one group and then performs the sequential iterations with the reduced number of unknowns. Every computing node has MATLAB functions as well as extended library implemented for the exchange of MATLAB data structure. The master implements another libraries such as threaded multiplication, partitioned inverse, and fast Jacobian to improve the speed of the serial execution part. The parallel library reduces the reconstruction time of image visualization about by half, while the distributed grouping scheme further reduces by about 12 times for the given target object when there are 4 computing nodes.

• Journal of the Society of Naval Architects of Korea
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• v.35 no.2
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• pp.29-37
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• 1998

Linear equations of motion for submersibles are one of the rest important design parameters, which are used as a governing equation for the shape design and the controller design. But, the estimated maneuvering coefficients in equations of motion by using empirical formulae, theoretical calculations or model tests might have some errors. Therefore the maneuvering coefficients should be verified from sea trial test. In this study, parallel extended Kalman filter method, Nelder & Mead Simplex method and genetic algorithm were applied to the parameter identification of roll maneuvering coefficients based on sea trial data. As a result, it was verified that Nelder & Mead Simplex method gave the most satisfactory results for the mathmatical models and the sea trial data used in this study.