• 한국정밀공학회지
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• 제29권10호
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• pp.1128-1136
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• 2012

In this paper, the states and parameters in a dynamic system are estimated by applying an Unscented Kalman Filter (UKF). The UKF is widely used in various fields such as sensor fusion, trajectory estimation, and learning of Neural Network weights. These estimations are necessary and important in determining the stability of a mobile system, monitoring, and predictions. However, conventional approaches are difficult to estimate based on the experimental data, due to properties of non-linearity and measurement noises. Therefore, in this paper, UKF is applied in estimating the states and parameters needed. An experimental dynamic system has been set up for obtaining data and the experimental data is collected for parameter estimation. The measurement noises are primarily reduced by applying the Low Pass Filter (LPF). Given the simulation results, the estimated error rate is 39 percent more efficient than the results obtained using the Least Square Method (LSM). Secondly, the estimated parameters have an average convergence period of four seconds.

• 한국산학기술학회논문지
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• 제13권2호
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• pp.772-778
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• 2012

본 논문은 비선형 시스템의 상태 추정에 널리 사용 되는 Unscented Kalman Filter(UKF)를 활용하여 동역학 시스템의 상태를 추정함과 동시에 파라미터를 추정하였다. 파라미터의 추정은 시스템 제어, 모델링, 성능분석 및 예측 등 다양한 분야에서 매우 중요하다. 공학에서 다루는 대부분의 시스템은 비선형성과 잡음이 존재하므로 파라미터 추정이 매우 어렵다. 이러한 경우에 대하여 본 논문에서는 비선형 필터로서 잡음에 강한 UKF를 이용하여 상태와 파라미터를 추정하였다. 본 논문에서 제안한 파라미터 추정은 기존의 상태방정식에 파라미터 항을 추가하여 확장된 비선형 방정식을 사용하였으며, 진자와 슬라이드로 구성된 2-자유도 동역학 시스템에 적용하였으며, 시스템 운동방정식의 측정 잡음으로 가우시안 잡음을 추가하여 컴퓨터 시뮬레이션을 실시하였다. 시뮬레이션 결과 제안한 방법이 LSM보다 좋은 성능을 보였다. 추정 오차는 3%이내이며, 0.1sec 이내의 수렴하는 것을 확인하였다. 결과적으로 UKF는 상태나 측정 데이터에 잡음이 존재하더라도 시스템의 상태 및 파라미터 추정이 가능하다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.191-196
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• 2006

In GPS Positioning, the error of satellite orbit will affect user's position accuracy directly, it is important to determine the satellite orbit precise. The real-time orbit is needed in kinematic GPS positioning, the precise GPS orbit from IGS would be delayed long time, so orbit prediction is key to real-time kinematic positioning. We analyze the GPS predicted ephemeris, on the base of comparison of EKF and UKF, a new orbit prediction method is put forward based on UKF in this paper, the result shows that UKF improves the orbit predicted precision and stability. It offers a new method for others satellites orbit determination as Galileo, and so on.

• Journal of international Conference on Electrical Machines and Systems
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• 제3권4호
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• pp.458-464
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• 2014

This paper proposes the rotor position and rotor speed estimation for an interior permanent magnet synchronous machines (IPMSM) using Unscented Kalman Filter (UKF) in alpha-beta coordinate system. Conventional algorithms using UKF are based on the simple observer model of IPMSM in d-q coordinate system. Rotor acceleration is neglected within the sampling step. An expansion of the observer model in an alpha-beta coordinate system with the consideration of the rotor speed variation provides the improved rotor position and speed estimation. The results show good stability concerning the expansion of observer model for the IPMSM.

• Smart Structures and Systems
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• 제14권6호
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• pp.1105-1129
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• 2014

The unscented Kalman filter (UKF) has been developed for nonlinear model parametric identification, and it assumes that the model parameters are symmetrically distributed about their mean values without any constrains. However, the parameters in many applications are confined within certain ranges to make sense physically. In this paper, a constrained unscented Kalman filter (CUKF) algorithm is proposed to improve accuracy of numerical substructure modeling in hybrid testing. During hybrid testing, the numerical models of numerical substructures which are assumed identical to the physical substructures are updated online with the CUKF approach based on the measurement data from physical substructures. The CUKF method adopts sigma points (i.e., sample points) projecting strategy, with which the positions and weights of sigma points violating constraints are modified. The effectiveness of the proposed hybrid testing method is verified by pure numerical simulation and real-time as well as slower hybrid tests with nonlinear specimens. The results show that the new method has better accuracy compared to conventional hybrid testing with fixed numerical model and hybrid testing based on model updating with UKF.

• International Journal of Control, Automation, and Systems
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• 제2권3호
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• pp.310-318
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• 2004

In this paper, an unscented Kalman filter (UKF) for curvilinear motions in an interacting multiple model (IMM) algorithm to track a maneuvering vehicle on a road is investigated. Driving patterns of vehicles on a road are modeled as stochastic hybrid systems. In order to track the maneuvering vehicles, two kinematic models are derived: A constant velocity model for linear motions and a constant-speed turn model for curvilinear motions. For the constant-speed turn model, an UKF is used because of the drawbacks of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1787-1788
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• 2006

In this paper, a noisy passive telemetry sensor system using Unscented Kalman Filter (UKF) is proposed. To overcome these trouble problems such as a power limitation and a estimation complexity that the general passive telemetry sensor system including IC chip has, the principle of inductive coupling was applied to the modelling of a passive telemetry sensor system (PTSS) and its noisy capacitive parameter was estimated by the UKF algorithm. Specialty, to show the effective tracking performance of the UKF, we compared with the tracking performance of Recursive Least Square Estimation (RLSE) using linearization

• 제어로봇시스템학회논문지
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• 제14권9호
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• pp.898-903
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• 2008

In most cases, the trajectory of a ballistic missile is well explained by the Kepler's laws. It implies that the remaining trajectory of the ballistic missile including its final destination can be easily predicted if the position and velocity vector of the ballistic missile at any point on its path can be exactly known. Hence, an effective tracking algorithm based on an exact radar measurement model is very important for developing Ballistic Missile Defense(BMD) system. In this paper, we address to design a nonlinear filter, Unscented Kalman Filter(UKF), to track the ballistic missile.

• 제어로봇시스템학회논문지
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• 제20권4호
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• pp.468-473
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• 2014

This paper describes the in-flight alignment of SDINS (Strapdown Inertial Navigation Systems) using an EKF (Extended Kalman Filter) and a UKF (Unscented Kalam Filter), which allow large initial attitude error uncertainty. Regardless of the inertial sensors, there are nonlinear error dynamics of SDINS in cases of large initial attitude errors. A UKF that is one of the nonlinear filtering approaches for IFA (In-Flight Alignment) are used to estimate the attitude errors. Even though the EKF linearized model makes velocity errors when predicting incorrectly in case of large attitude errors, a UKF can represent correctly the velocity errors variations of attitude errors with nonlinear attitude error components. Simulation results and analyses show that a UKF works well to handle large initial attitude errors of SDINS and the alignment error attitude estimation performance are quite improved.

• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.1212-1220
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• 2015

This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KRJUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, dual-EKF, joint-UKF and KR-JUKF are applied to a CSTR with cooling jacket, in which production of propylene glycol happens and performance of KR-JUKF is evaluated.