• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.4B
• /
• pp.331-337
• /
• 2002

In this paper we propose extended Kalman filter (EKF)-trilateration method which associates EKF to conventional radiolocation technique, trilateration in order to improve the accuracy of dynamic orbit determination of geostationary satellite for broadcasting of highly accurate Standard Time/Frequency Signal (STFS). We then compare to analyze the time accuracies of three techniques which are differential mode, trilateration locating one of four calibrated earth stations on a neighboring country, and domestic-only baseline EKF-trilateration. Computer simulations have shown that in spite of domestic-only baseline EKF-trilateration of poor GDOP, it is possible to track and locate satellite with an accuracy of a few hundred meter which is the performance 10 times more accurate than trilateration can provide. Finally we can provide standard time service with the time accuracy better than a few ns (frequency stability : 10$\^$-14/ over 7 days) all around Korea peninsula.

• Journal of the Korean Society for Precision Engineering
• /
• v.32 no.5
• /
• pp.471-481
• /
• 2015

This paper presents the robot vision control schemes using Extended Kalman Filter (EKF) method for the slender bar placement in the appearance of obstacles during robot movement. The vision system model used for this study involves the six camera parameters($C_1{\sim}C_6$). In order to develop the robot vision control scheme, first, the six parameters are estimated. Then, based on the estimated parameters, the robot's joint angles are estimated for the slender bar placement. Especially, robot trajectory caused by obstacles is divided into three obstacle regions, which are beginning region, middle region and near target region. Finally, the effects of number of obstacles using the proposed robot's vision control schemes are investigated in each obstacle region by performing experiments of the slender bar placement.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.05a
• /
• pp.303-306
• /
• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKF uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

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

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.42 no.11
• /
• pp.937-946
• /
• 2014

This paper presents system identification of a single tilt wing UAV. A Modified Equation Error Method(MEEM) and Extended Kalman Filter(EKF) are used for the identification of a single tilt wing UAV system in frequency-domain and time-domain, respectively. Simulated flight data is obtained from CNUX-3's vertical mode linear simulation with realistic sensor noise. System identification performance is analyzed with respect to a variety of design parameters of the MEEM. Also, High accuracy Fourier Transform(HFT) is applied to enhance the performance of MEEM. The results of the MEEM is compared with those of the EKF. Design parameters of the MEEM and initial conditions of the EKF are decided from optimization.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.8
• /
• pp.780-788
• /
• 2006

This paper deals with INS/GPS tightly coupled integration algorithms using extend Kalman filter (EKF) and unscented Kalman filter (UKF). In the tightly coupled approach, nonlinear pseudorange measurement models are used for the INS/GPS integration Kalman filter. Usually, an EKF is applied for this task, but it may diverge due to poor functional linearization of the nonlinear measurement. The UKF approximates a distribution about the mean using a set of calculated sigma points and achieves an accurate approximation to at least second-order. We introduce the generalized scaled unscented transformation which modifies the sigma points themselves rather than the nonlinear transformation. The generalized scaled method is used to transform the pseudo range measurement of the tightly coupled approach. To compare the performance of the EKF- and UKF-based tightly coupled approach, real van test and simulation have been carried out with feedforward and feedback indirect Kalman filter forms. The results show that the UKF and EKF have an identical performance in case of the feedback filter form, but the superiority of the UKF is demonstrated in case of the feedforward filer form.

• Journal of Korea Game Society
• /
• v.12 no.3
• /
• pp.87-96
• /
• 2012

This paper presents a novel EKF(Extended Kalman Filter) based SLAM(Simultaneous Localization And Mapping) system for stable camera tracking and re-localization. The obtained 3D points by SLAM are triangulated using Delaunay triangulation to establish a reference plane, and features are described by BRISK(Binary Robust Invariant Scalable Keypoints). The proposed method estimates the camera parameters from the homography of the reference plane when the tracking errors of EKF SLAM are much accumulated. Using the robust descriptors over sequence enables us to re-localize the camera position for matching over sequence even though the camera is moved abruptly.

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

• International Journal of Aeronautical and Space Sciences
• /
• v.14 no.1
• /
• pp.85-90
• /
• 2013

This paper presents a study on terrain referenced navigation (TRN). The extended Kalman filter (EKF) is adopted as a filter method. A Jacobian matrix of measurement equations in the EKF consists of terrain slope terms, and accurate slope estimation is essential to keep filter stability. Two slope estimation methods are proposed in this study. Both methods are based on the least-squares approach. One is planar regression searching the best plane, in the least-squares sense, representing the terrain map over the region, determined by position error covariance. It is shown that the method could provide a more accurate solution than the previously developed linear regression approach, which uses lines rather than a plane in the least-squares measure. The other proposed method is weighted planar regression. Additional weights formed by Gaussian pdf are multiplied in the planar regression, to reflect the actual pdf of the position estimate of EKF. Monte Carlo simulations are conducted, to compare the performance between the previous and two proposed methods, by analyzing the filter properties of divergence probability and convergence speed. It is expected that one of the slope estimation methods could be implemented, after determining which of the filter properties is more significant at each mission.

• Journal of Advanced Navigation Technology
• /
• v.21 no.1
• /
• pp.138-144
• /
• 2017

A method for improving the integrated navigation performance in the INS/GPS navigation system by the considering that the condition that the geometric arrangement of the satellite is degraded due to limitation of the line of sight of the satellite such as geographic feature and GPS signal jamming is proposed. A variable covariance extended Kalman filter (VCEKF) that correlated to the measured covariance to the DOP of GPS is suggested. The navigation performance of integrated navigation system using EKF and VCEKF is analyzed by Monte-Carlo simulations. The result is verified that VCEKF has better estimation performance than EKF using fixed covariance on condition that DOP value is larger than the smaller value.