• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.232-238
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• 2013

This paper study the position estimation of MBK system according to the non-linear filter for non-Gaussian noise in underwater sensor networks. In the filter to estimate location, recently, the extended Kalman filter (EKF) and particle filter are getting attention. EKF is widely used due to the best algorithm in the Gaussian noise environment, but has many restrictions on the usage in non-Gaussian noise environment such as in underwater. In this paper, we propose the improved One-Dimension Particle Filter (ODPF) using the distribution re-interpretation techniques based on the maximum likelihood. Through the simulation, we compared and analyzed the proposed particle filter with the EKF in non-Gaussian underwater sensor networks. In the case of both the sufficient statistical sample and the sufficient calculation capacity, we confirm that the ODPF's result shows more accurate localization than EKF's result.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.5
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• pp.43-52
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• 2003

Two methods which estimate manoeuvring derivatives in the model of hydrodynamic force and moment acting on a manoeuvring ship using sea trial data were compared. One is the widely used parameter estimation method by using the Extended Kalman Filter (EKF), which estimates state variables of linearized state space model at every instant after dealing with the coefficients as the augmented state variables. The other one is the Estimation-Before-Modeling (EBM) technique, so called the two-step method. In the first step, hydrodynamic force of which dynamic model is assumed the third-order Gauss-Markov process is estimated along with motion variables by the EKF and the modified Bryson-Frazier smoother. Then, in the next step, manoeuvring derivatives are identified through the regression analysis. If the exact structure of hydrodynamic force could be known, which was an ideal case, the EKF method would be regarded as being more superior compared to the EBM technique. However the EBM technique was more robust than the EKF method from a realistic point of view where the assumed model structure was slightly different from the real one.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.256-263
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• 2002

In this study, the tracking of the temporal variation of the frequency and the amplitude in the presence of additive white Gaussian noise is considered using the Extended Kalman filter (EKF. The EKF has many applications and it has been applied to the problem of tracking the time-variable frequency. However the existing EKF frequency trackers could was driven in the small time-variable amplitude or required the additional amplitude tracker in the large time-variable amplitude. In this study, the EKF frequency-amplitude tracker, which could track both frequency and amplitude simultaneously from the measured signal in the relatively large time-variable amplitude environment, is proposed for improving the performance of the time-variable frequency tracking and its performance is verified by the simulation and the experimental work.

• Journal of Ocean Engineering and Technology
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• v.19 no.2 s.63
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• pp.82-89
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• 2005

This study deals with the development of the extended Kalman filter(EKF) algorithm for the localization of underwater mining vehicles. Both simulation and experimental studies in a test bed are carried out. For the experiments, a scale dawn tracked vehicle is run in a soil bin containing cohesive soil of bentonite-water mixture. To develop the EKF algorithm, we use a kinematic model including the inner/outer track slips and the slip angle for the vehicle. The measurements include the inner and outer wheel speeds from encoders, the heading angle from a compass sensor and a fiber optic rate gyro, and x and y coordinate position values from a vision system. The vision sensor replaces the LBL(Long Base Line) sonar system used in the real underwater positioning situations. Artificial noise signals mimicking the real LBL noise signal are added to the vision sensor information. To know the mean slip values of the tracks in both straight and cornering maneuver, several trial running experiments are executed before applying the EKF algorithm. Experimental results show the effectiveness of the EKF algorithm in rejecting the sensor measurements noise. Also, the simulation and experimental results show close correlations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.10
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• pp.1989-1996
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• 2002

Extended Kalman Filter(EKF) is used to estimate friction and spring characteristics on the swash plate of a variable displacement pump. In earlier studies, the feasibility of the approach was established using simulation studies to establish limits of accuracy for the EKF approach when it was applied to an ideal situation. In this study, the EKF is applied to an experimental system and the issue of re liability in estimation of certain pump parameters is addressed. In addition, an approach to assign values to accommodate convergence of the EKF is considered. A special experimental system was set up to facilitate the measurement of certain states to enhance the EKF approach. Estimated parameters show ed some scatter about a specified operating point but in general, were reasonably repeatable. The study also showed that changes in the system parameters could be accurately tracked.

• 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.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1141-1143
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• 2001

This paper presents a detailed study of the reduced-model extended Kalman filter(EKF) for estimating the rotor speed of an induction motor drive. The general structure of the Kalman filter is reviewed and the various system vectors and matrices are defined. By including the rotor speed as a state variable, the EKF equations are established from a discrete two axis model of the three-phase induction motor, using the software MATLAB/Simulink, simulation of the EKF speed estimation algorithm is carried out for an induction motor drive with indirect vector control.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.269-274
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• 1986

A continuous-time modified gain extended Kalman filter (MGEKF) is developed in an effort to extend the discrete-time results of 1) and 2). Used as an observer, it is globally exponentially convergent. For stochastic system, the stability of the MGEKF is proven under certain conditions. The performance of the MGEKF is compared with that of the EKF for a particular nonlinear system where the fininate dimensional optimal filter exists.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.4
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• pp.345-354
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• 2014

In this paper, we propose a real-time algorithm for estimating the relative position and velocity of a person with respect to a robot using a Kinect sensor and an extended Kalman filter (EKF). Additionally, we propose an algorithm for controlling the robot in the proximity of a person in a variety of modes. The algorithm detects the head and shoulder regions of the person using a histogram of oriented gradients (HOG) and a support vector machine (SVM). The EKF algorithm estimates the relative positions and velocities of the person with respect to the robot using data acquired by a Kinect sensor. We tested the various modes of proximity movement for a human in indoor situations. The accuracy of the algorithm was verified using a motion capture system.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.671-674
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• 1999

In the PWM converter, PLL(Phase Locked Loop) is usually used as a tool which senses the angle of input voltage. This is sensitive to nois and needs additional hardware. In this work, we propose a sensorless control scheme of PWM converter using EKF(Extended Kalman Filter). EKF estimates a phase angle of input voltage from nonlinear state equation using measured phase currents. We control power factor and DC-link voltage utilizing the estimated phase angle. We demonstrate the effectiveness of the proposed estimation algorithm through simulations.