• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.188-188
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• 2000

The most important problem in target tracking can be said to be modeling the tracking system correctly. Although the simple linear dynamic equation for this model has used until now, the satisfactory performance could not be obtained owing to uncertainties of the real systems in the case of designing the filters baged on the dynamic equations. In this paper, we propose the extended robust Kalman filter (ERKF) which can be applied to the real target tracking system with the parameter uncertainties. A nonlinear dynamic equation with parameter uncertainties is used to express the uncertain system model mathematically, and a measurement equation is represented by a nonlinear equation to show data from the radar in a Cartesian coordinate frame. To solve the robust nonlinear filtering problem, we derive the extended robust Kalman filter equation using the Krein space approach and sum quadratic constraint. We show the proposed filter has better performance than the existing extended Kalman filter (EKF) via 3-dimensional target tracking example.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.561-564
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• 2004

This paper proposes a new application of Kalman filter to estimate speed sensorless DC motor. Kalman filter can estimate the system state variables accurately; even the system input is disturbed with noise. In the design, the mathematical model of DC motor in discrete state-space form will be created; the speed of DC motor which is considered as state variable and can be estimated by using Kalman filter. In the experiment; TMS320C31 floating point digital signal processor is used for hardware implementation, the input is disturbed with/and without white noise in the experiment. The experimental results show the speed of DC motor which is estimated by Kalman filter has good accuracy when compared with the results from tacho-meter.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.905-908
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• 1988

In recent, Kalman filter technique has been much used as one of technique for tracking of the moving target. But some problem are still remained to be resolved. For example, when Kalman filter technique is applied to nonlinear system, the technique is nonoptimal estimator. Therefore, extended Kalman filter is proposed to reduce modeling error for nonlinear system. In this study, an extended Kalman filter in Cartesian coordinates is described for moving target, when the radar sensor measures range, azimuth and elevation angle in polar coordinates. And an approximate gain computation algorithm is proposed. In this approach, Kalman gains are computed for three uncoupled filter and multiplied by a Jacobian transformation determined from the measured target position and orientation.

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.71-82
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• 2006

This paper propose a realtime tracking algorithm of mobile object in indoor environment. this proposed system selects location data closer to mobile objects in real time that are results of Triangulation method and DCM(Database Correlation Method)method. Also, this system applies adjusted location data selected by using Kalman filter, and in result it improved location accuracy of transfer object. Be studied in existing the Kalman filter have unstable location data until its settlement because of it extracts current values by using the past the information. However, proposed location tracking system don't apply existent Kalman filter to this system and it permits precisional tracking location by uses more effective methods.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.372-379
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• 2014

This paper surveys past and state-of-the-art SLAM technologies. The standard methods for solving the SLAM problem are the Kalman filter, particle filter, graph, and bundle adjustment-based methods. Kalman filters such as EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter) have provided successful results for estimating the state of nonlinear systems and integrating various sensor information. However, traditional EKF-based methods suffer from the increase of computation burden as the number of features increases. To cope with this problem, particle filter-based SLAM approaches such as FastSLAM have been widely used. While particle filter-based methods can deal with a large number of features, the computation time still increases as the map grows. Graph-based SLAM methods have recently received considerable attention, and they can provide successful real-time SLAM results in large urban environments.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.445-448
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• 2003

In this paper, we present a hierarchical approach of an enhanced active shape model for video tracking. Kalman filter is used. To estimate a dynamic shape in video object tracking. The experimental results show that the proposed hierarchical active shape model using Kalman filter is efficient.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.499-506
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• 2004

The Unscented Kalman Filter is known to outperform the Extended Kalman Filter for the nonlinear state estimation with a significance advantage that it does not require the computation of Jacobian but EKF has a competitive advantage to the UKF on the performance time. We compare both algorithms on training the artificial neural network. The validation data set is used to estimate parameters which are supposed to result in better fitting for the test data set. Experimental results are presented which indicate the performance of both algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.2047-2051
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• 2009

In this paper, we propose the localization method on WSN(Wireless Sensor Network) using fuzzy model and Kalman filter. The proposed method is as follows: First, we estimate the distance of RSSI(Receive Signal Strength Index) by using fuzzy model in order to minimize the distance error. Second, we use a triangulation measurement for estimating the localization. And then, we minimize the localization error using a Kalman filter. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.302-304
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• 2016

The purpose of this paper is to draw comparison between the performance of ${\alpha}-{\beta}-{\gamma}$ filter and Kalman filter of a tracking module for ARPA system on board high dynamic warship. The comparison is based on the filters' capability to reduce residual error and maintain a stable transient response. The residual error is computed from the difference between the observed the predicted positions for the entire tracking period. The results indicate that the Kalman filter has a higher tracking accuracy compared to the optimal ${\alpha}-{\beta}-{\gamma}$ filter. However, both filters have a similar transient response.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.3
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• pp.52-58
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• 2009

If a moving target has a linear characteristics, the Kalman filter can estimate relatively accurate the location of a target, but this performance depends on how the dynamic status characteristics of the target is accurately modeled. In many practical problems of tracking a maneuvering target, a simple kinematic model can fairly accurately describe the target dynamics for a wide class of maneuvers. However, since the target can exhibit a wide range of dynamic characteristics, no fixed SKF(Simple Kalman filter) can be matched to estimate, to the required accuracy, the states of the target for every specific maneuver. In this paper, a new AKF(Active Kalman filter) is proposed to solve this problem The process noise covariance level of the Kalman filter is adjusted at each time step according to the study result which uses the neural network algorithm. It is demonstrated by means of a computer simulation that the tracking capability of the proposed AKF(Active Kalman filter) is better than that of the SKF(Simple Kalman Filter).