• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.288-289
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• 2007

본 논문에서는 통계적 파라미터 불확실성을 포함한 시변 선형 불확정 시스템에 대한 강인 칼만필터링 문제를 고려한다. 최소자승 관점에서 정의된 공칭 칼만필터링 문제의 목적함수를 파라미터 불확실성의 통계적 특성을 이용하여 가용한 측정행렬의 함수로 표현하고, 이로부터 근사화된 선형공간 위로의 사교사영으로 해를 도출할 수 있음을 보인다. 최종적으로 벡터 최소자승 추정기법을 동일하게 적용하여, 순환강인 칼만필터식을 유도하고, 유도된 강인 칼만필터 식이 최근 제안된 강인 최소자승 추정식에 공정잡음 및 측정잡음 분산을 반영한 보완된 형태임을 확인한다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.59-66
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• 2005

In this paper, a robust stationary Kalman filter is designed by minimizing selected performance indices so that it is less sensitive to uncertainties. The uncertainties include not only stochastic factors such as process noise and measurement noise, but also deterministic factors such as unknown initial estimation error, modeling error and sensing bias. To reduce the effect on the uncertainties, three performance indices that should be minimized are selected based on the quantitative error analysis to both the deterministic and the stochastic uncertainties. The selected indices are the size of the observer gain, the condition number of the observer matrix, and the estimation error variance. The observer gain is obtained by optimally solving the multi-objectives optimization problem that minimizes the indices. The robustness of the proposed filter is demonstrated through the comparison with the standard Kalman filter.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.1
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• pp.43-48
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• 2012

This paper proposes a practical linear recursive robust motion deblurring filter using the inertial sensor measurements for strapdown image seekers. The angular rate information obtained from the gyro mounted on the missile is used to define the PSF(point spread function). Since the gyro output contains a unknown but bounded bias error. the motion blur image model can be expressed as the linear uncertain system. In consequence, the motion deblurring problem can be cast into the robust Kalman filtering which provides reliable state estimates even in the presence of the parametric uncertainty due to the gyro bias. Through the computer simulations using the actual IR scenes, it is verified that the proposed algorithm guarantees the robust motion deblurring performance.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.543-549
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• 2021

In this paper, a robust transfer alignment method is proposed for a strapdown inertial navigation system(SDINS) with norm-bounded parametric uncertainties. The uncertainties are described by the energy bound constraint, i.e., sum quadratic constraint(SQC). It is shown that the SQC can be coverted into an indefinite quadratic cost function in the Krein space. Krein space Kalman filter is designed by modifying the measurement matrix and the variance of measurement noises in the conventional Kalman filter. Since the proposed Krein space Kalman filter has the same recursive structure as a conventional Kalman filter, the proposed filter can easily be designed. The simulation results show that the proposed filter achieves robustness against measurement time delay and high dynamic environment of the vehicle.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.104-109
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• 2002

A new robust Kalman filter is designed for the linear discrete-time system with norm-bounded parametric uncertainties. Sum quadratic constraint, which describes the uncertainties of the system, is converted into an indefinite quadratic form to be minimized in indefinite inner product space. This minimization problem is solved by the new robust Kalman filter. Since the new filter is obtained by simply modifying the conventional Kalman filter, robust filtering scheme can be more readily designed using the proposed method in comparison with the existing robust Kalman filters. A numerical example demonstrates the robustness and the improvement of the proposed filter compared with the existing filters.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.15-23
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• 2017

This paper reports a SOC(State-of-Charge) estimation method using the extended Kalman filter(EKF) algorithm, which can allow real-time implementation and reduce the error of the model and be robust against noise, to accurately estimate and evaluate the charging/discharging state of the EV(Electric Vehicle) battery. The battery was modeled as the first order Thevenin model for the EKF algorithm and the parameters were derived through experiments. This paper proposes the changed method, which can have the SOC to 0% ~ 100% regardless of the aging of the battery by replacing the rated capacity specified in the battery with the maximum chargeable capacity. In addition, This paper proposes the EKF algorithm to estimate the non-linearity interval of the battery and simulation result based on Ah-counting shows that the proposed algorithm reduces the estimation error to less than 5% in all intervals of the SOC.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.4
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• pp.274-282
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• 2009

Various location tracking sensors; such as GPS, INS, radar, and optical equipment; are used for tracking moving targets. In order to effectively track moving targets, it is necessary to develop an effective fusion method for these heterogeneous devices. There have been studies in which the estimated values of each sensors were regarded as different models and fused together, considering the different error characteristics of the sensors for the improvement of tracking performance using heterogeneous multi-sensor. However, the rate of errors for the estimated values of other sensors has increased, in that there has been a sharp increase in sensor errors and the attempts to change the estimated sensor values for the Sensor Probability could not be applied in real time. In this study, the Sensor Probability is obtained by comparing the RMSE (Root Mean Square Error) for the difference between the updated and measured values of the Kalman filter for each sensor. The process of substituting the new combined values for the Kalman filter input values for each sensor is excluded. There are improvements in both the real-time application of estimated sensor values, and the tracking performance for the areas in which the sensor performance has rapidly decreased. The proposed algorithm adds the error characteristic of each sensor as a conditional probability value, and ensures greater accuracy by performing the track fusion with the sensors with the most reliable performance. The trajectory of a UAV is generated in an experiment and a performance analysis is conducted with other fusion algorithms.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.190-196
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• 2015

In this paper, we propose multiple location error compensation algorithm for GPS/INS fusion using kalman filter and introduce the way to reduce location error in 9-axis navigation devices for implementing inertial navigation technique. When evaluating location, there is an increase of location error. So navigation systems need robust algorithms to compensate location error in GPS/INS fusion. In order to improve robustness of 9-axis inertial sensor(mpu-9150) over its disturbance, we used tilt compensation method using compensation algorithm of acceleration sensor and Yaw angle compensation to have exact azimuth information of the object. And it shows improved location result using these methods combined with kalman filter.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1866-1867
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• 2006

In this paper, a robust Extended Kalman filter is proposed by introducing a new sliding mode surface. This filter can be used for the system with a matching condition The new state estimater is designed for stochastic systems with bounded uncertainties

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2477-2479
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• 2005

A robust bending frequency tracker is proposed to design the adaptive notch filter which removes the time-varying missile structural modes from the sensor measurements. To do this, the state-space form of a bending frequency model is derived under the assumption that the bending signal could be described as the lightly damped sinusoid. Since the resultant bending frequency model contains the parametric uncertainties in the measurement matrix, the design problem of bending frequency tracker is tackled by applying the robust Kalman filter to the model. This technique could be easily expanded to the multiple frequencies case because it newly illuminates the bending frequency tracking problem in view of general state estimation.