• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.264-267
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• 1991

In this paper, a new application method of the Kalman filter to desigin a gyro is proposed. The role of a gyro is the estimation of an input rate with minimal error covariance. The size of the error covariance depends on gyro's parameters, which makes it possible to use the parameters of gyro to minimze the estimation error covariance. Numerical analysis shows that the error covariance becomes smaller as the spin axis momentum becomes larger and the damping coefficient smaller, but production cost must be considered. Through numerical analysis the parameter set for an acceptable - performance gyro with small cost can be selected.

• 디지털산업정보학회논문지
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• 제10권4호
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• pp.53-61
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• 2014

In this paper, we proposed covariance weight matrix using SPT matrix in order to accurate target estimation. We have estimated a target using modified covariance matrix and beam steering error method. We have minimized beam steering error in order to estimation desired a target. This method obtain optimum covariance weight using modified SPT matrix. This paper of proposal method is showed good performance than general method. We updated a weight of covariance matrix using modified SPT matrix. We obtain optimum covariance matrix weight to application beam steering error method in order to beam steering toward desired target. Through simulation, we showed that compare proposal method with general method. It have improved resolution of estimation target to good performance more proposed method than general method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.34-39
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• 1993

Presented in this paper is a complete error covariance analysis for strapdown inertial navigation system(SDINS). We have found that in SDINS the cross-coupling terms in gyrocompass alignment errors can significantly influence the SDINS error propagation. Initial heading error has a close correlation with the east component of gyro bias erro, while initial level tilt errors are closely related to accelerometer bias errors. In addition, pseudo-state variables are introduced in covariance analysis for SDINS utilizing the characteristics of gyrocompass alignment errors. This approach simplifies the covariance analysis because it makes the initial error covariance matrix to a diagonal form. Thus a real implementation becomes easier. The approach is conformed by comparing the results for a simplified case with the covariance analysis obtained from the conventional SDINS error model.

• 대기
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• 제23권1호
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• pp.33-37
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• 2013

In meteorology, exploitation of Kalman filter as a data assimilation system is virtually impossible due to simultaneous requirements of adjoint model and large computer resource. The other substitute of utilizing ensemble Kalman filter is only affordable by compensating an enormous usage of computing resource. Furthermore, the latter employs ensemble integration sets for evolving the background error covariance matrix by compensating the dynamical feature of the temporal evolution of weather conditions. We propose a new implementation method that works without the adjoint model by utilizing the explicit expression of the background error covariance matrix in backward evolution. It will also break a barrier in the evolution of the covariance matrix. The method may be applied with a slight modification to the real time assimilation or the retrospective analysis.

• International Journal of Aeronautical and Space Sciences
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• 제18권3호
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• pp.555-564
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• 2017

In this paper, a new technique for attitude and heading reference system (AHRS) using low-cost MEMS sensors of the gyroscope, accelerometer, and magnetometer is addressed particularly in vibration environments. The motion of MEMS sensors interact with the scale factor and cross-coupling errors to produce random errors by the harsh environment. A new adaptive attitude estimation algorithm based on the Kalman filter is developed to overcome these undesirable side effects by analyzing windowed measurement error covariance. The key idea is that performance degradation of accelerometers, for example, due to linear vibrations can be reduced by the proposed measurement error covariance analysis. The computed error covariance is utilized to the measurement covariance of Kalman filters adaptively. Finally, the proposed approach is verified by using numerical simulations and experiments in an acceleration phase and/or vibrating environments.

• 한국항행학회논문지
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• 제8권1호
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• pp.26-37
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• 2004

차분 위성 항법에 있어서 빈번한 동적 변화를 수반하는 항체에 대하여 편향되지 않은 위치 추정치를 생성하기 위해서는 위상 평활화 코드 필터가 널리 사용되고 있다. 위치추정, 오차해석, 고장진단, 그리고 미지정수 결정 등 보정위성항법시스템의 다양한 응용분야에 있어서 정확한 오차공분산 정보는 중요한 역할을 담당한다. 반면, 기존의 위상 평활화 코드 필터 알고리즘들은 대부분 누적위상 측정오차에 의한 시전달 오차를 무시하므로 실재에 비하여 낙관적인 오차공분산 정보를 생성할 위험성을 내포하고 있다. 위상 평활화 코드 기법의 활용에 있어서 일관성 있고 적절한 오차공분산 정보를 생성하기 위하여 본 논문에서는 Stepwise Optimal Position Projection Filter와 Stepwise Unbiased Position Projection Filter 알고리즘을 제안하였다. 제안된 필터는 기존의 필터에 비하여 누적위상의 특성에 기인하는 시전달 오차의 특성을 정확하고 상세하게 고려하여 주며 잦은 가시위성의 변화도 함께 고려할 수 있는 장점을 가진다. 몬테카를로 시뮬레이션에 의하여 수신기 Kalman 필터, 기존의 위상 평활화 코드 필터, 그리고 제안된 두 필터들의 성능을 비교 분석 하였다.

• 제어로봇시스템학회논문지
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• 제12권2호
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• pp.93-100
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• 2006

In this paper, a new-type Extended Kalman Filter (EKF) is proposed as a robust nonlinear filter for a stochastic nonlinear system. The original EKF is widely used for various nonlinear system applications. But it is fragile to its estimation errors because they give rise to linearization errors that affect the system mode1 as the modeling errors. The linearization errors are nonlinear functions of the estimation errors therefore it is very difficult to obtain the accurate error covariance of the EKF using the linear form. The inaccurately estimated error covariance hinders the EKF from being a sub-optimal estimator. The proposed filter tries to obtain the upper bound of the error covariance tolerating the uncertainty of the error covariance instead of trying to obtain the accurate one. It treats the linearization errors as uncertain modeling errors that can be handled by the robust linear filtering. In order to be more robust to the estimation errors than the original EKF, the proposed filter minimizes the upper bound like the robust linear filter that is applied to the linear model with uncertainty. The in-flight alignment problem of the inertial navigation system with GPS position measurements is a good example that the proposed robust filter is applicable to. The simulation results show the efficiency of the proposed filter in the robustness to initial estimation errors of the filter.

• International Journal of Aeronautical and Space Sciences
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• 제1권1호
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• pp.70-80
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• 2000

The attitude knowledge error model is formulated for specifically KOMPSAT attitude determination system using the Lefferts/Markley/Shuster method, and the attitude determination(AD) error analysis is performed so as to investgate the on-board attitude determination capability of KOrea Multi-Purpose SATellite(KOMPSAT) using the covariance analysis method. Analysis results show there is almost no initial value effect on Attitude Determination (AD) error and the sensor noise effects on AD error are drastically decreased as is predicted because of the inherent characteristic of Kalman filter structure. However, it shows that the earth radiance effect of IR-sensor(earth sensor) and the bias effects of both IR-sensor and fine sun sensor are the dominant factors degrading AD error and gyro rate bias estimate error in AD system. Analysis results show that the attitude determination errors of roll, pitch and yaw axes are 0.056, 0.092 and 0.093 degrees, respectively. These numbers are smaller than the required values for the normal mission of KOMPSAT. Also, the selected on-orbit data of KOMPSAT is presented to demonstrate the designed AD system.

• Ocean and Polar Research
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• 제30권2호
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• pp.129-140
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• 2008

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.

• 한국컴퓨터정보학회논문지
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• 제15권5호
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• pp.13-21
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• 2010

도립진자 시스템에서 칼만 필터링 최적의 결과를 얻기 위해서는 잡음 공분산 행열 Q, 측정잡음 공분산 행열 R과 초기 에러 공분산 행열 $P_0$와 같은 인자가 필요하다. 이러한 인자는 실제 상황에서 근사화된 값을 사용하거나 정확한 값을 알 수 없기 때문에 칼만 필터의 최적화에 영향을 미치지 않거나 이러한 공분산 행열의 스칼라 이득변화에 덜 민감한 경우를 연구의 대상으로 하고 있다. 또한 상태 측정시 에러를 예측하는 방법으로 구해진 에러 공분산 행열은 상태측정 값 보다는 공분산 행열의 이득과 연관성을 가지게 된다. 따라서 3가지 공분산 행열과 칼만 이득 그리고 에러 공분산 행열 간의 상관관계가 잡음인자인 스칼라 이득과의 연관성을 해석하고자 하였다. 본 연구는 3절에서 도립진자 시스템 모델을 간략하게 정리를 하였고 4절에서는 이러한 모델을 기반으로 하여 컴퓨터 시뮬레이션을 위한 도립진자 시스템에 대한 수학적 동적모델을 구성하고 5절에서는 이러한 인자와 스칼라 이득 값을 이용한 다양한 시뮬레이션 결과를 통하여 잡음인자의 연관성을 해석하였다.