• 전기학회논문지
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• 제65권6호
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• pp.1019-1025
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• 2016

This paper proposes a parallel reduced-order square-root unscented Kalman filter for state estimation of a sensorless permanent-magnet synchronous motor. The appearance of an unscented Kalman filter is caused by the linearization process error between a real system and classical Kalman model. The unscented transformation can make a more accurate Kalman model. However, the complexity is its main drawback. This paper investigates the design and implementation of the proposed filter with Potter and Carlson square-root form. The proposed parallel reduced-order square-root unscented Kalman filter reduces memory and code size, and improves numerical computation. And the performance is not significantly different from the unscented Kalman filter. The experimentation is performed for the verification of the proposed filter.

• 한국시뮬레이션학회논문지
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• 제27권3호
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• pp.99-107
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• 2018

본 논문에서는 비선형성을 가지는 측정방정식의 상태값을 효과적으로 추정할 수 있는 확장칼만필터(Extended Kalman Filter/EKF)와 확장칼만필터의 변종들 그리고 코스트 레퍼런스 파티클필터(Cost-Reference Particle Filter/CRPF)를 이용하여 이차원 공간에서 표적추적 성능에 관하여 연구한다. 확장칼만필터의 변종으로 분산점칼만필터(Unscented Kalman Filter/UKF), 중심차분칼만필터(Central Difference Kalman Filter/CDKF), 제곱근 분산점칼만필터(Square Root Unscented Kalman Filter/SR-UKF) 그리고 제곱근 중심차분칼만필터(Square Root Central Difference Kalman Filter/SR-CDKF)를 소개한다. 본 연구에서는 노이즈가 불확실한 표적에 대하여 몬테카를로 시뮬레이션 기법을 이용하여 각 필터들의 평균제곱오차(Mean Square Error/MSE)를 계산하였다. 시뮬레이션 결과 확장칼만필터의 변종들 중에서 제곱근 중심차분칼만필터가 속도와 성능 면에서 가장 우수한 결과를 보여주었다. 코스트 레퍼런스 파티클 필터는 확장칼만필터와 다르게 노이즈의 확률 분포를 알 필요가 없다는 유리한 특성을 가지고 있으며 시뮬레이션 결과 제곱근 중심차분칼만필터보다 처리속도 및 정확도 면에서 우수한 결과를 보여주었다.

• 제어로봇시스템학회논문지
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• 제14권9호
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• pp.865-872
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• 2008

Spacecraft attitude estimation using the nonlinear unscented filter is addressed to fully utilize capabilities of the unscented transformation. To release significant computational load, an efficient technique is proposed by reasonably removing correlation between random variables. This modification introduces considerable reduction of sigma points and computational burden in matrix square-root calculation for most nonlinear systems. Unscented filter technique makes use of a set of sample points to predict mean and covariance. The general QUEST(QUaternion ESTimator) algorithm preserves explicitly the quaternion normalization, whereas extended Kalman filter(EKF) implicitly obeys the constraint. For spacecraft attitude estimation based on quaternion, an approach to computing quaternion means from sampled quaternions with guarantee of the quaternion norm constraint is introduced applying a constrained optimization technique. Finally, the performance of the new approach is demonstrated using a star tracker and rate-gyro measurements.

• 한국자동차공학회논문집
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• 제20권6호
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• pp.126-131
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• 2012

Tire force is one of important parameters which determine vehicle dynamics. However, it is hard to measure tire force directly through sensors. Not only the sensor is expensive but also installation of sensors on harsh environments is difficult. Therefore, estimation algorithms based on vehicle dynamic models are introduced to estimate the tire forces indirectly. In this paper, an estimation system for estimating lateral force and states is suggested. The state-space equation is constructed based on the 3-DOF bicycle model. Extended Kalman Filter, Unscented Kalman Filter and Ensemble Kalman Filter are used for estimating states on the nonlinear system. Performance of each algorithm is evaluated in terms of RMSE (Root Mean Square Error) and maximum error.

• Journal of Power Electronics
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• 제18권4호
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• pp.1127-1139
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• 2018

A reduced particle-unscented Kalman filter estimation method, along with a splice-equivalent circuit model, is proposed for the state-of-charge estimation of an aeronautical lithium-ion battery pack. The linearization treatment is not required in this method and only a few sigma data points are used, which reduce the computational requirement of state-of-charge estimation. This method also improves the estimation covariance properties by introducing the equilibrium parameter state of balance for the aeronautical lithium-ion battery pack. In addition, the estimation performance is validated by the experimental results. The proposed state-of-charge estimation method exhibits a root-mean-square error value of 1.42% and a mean error value of 4.96%. This method is insensitive to the parameter variation of the splice-equivalent circuit model, and thus, it plays an important role in the popularization and application of the aeronautical lithium-ion battery pack.

• ETRI Journal
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• 제38권3호
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• pp.579-588
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• 2016

To improve the ability to determine a vehicle's movement information even in a challenging environment, a hybrid approach called non-Gaussian square rootunscented particle filtering (nGSR-UPF) is presented. This approach combines a square root-unscented Kalman filter (SR-UKF) and a particle filter (PF) to determinate the vehicle state where measurement noises are taken as a finite Gaussian kernel mixture and are approximated using a sparse Gaussian kernel density estimation method. During an outage-free GPS period, the updated mean and covariance, computed using SR-UKF, are estimated based on a GPS observation update. During a complete GPS outage, nGSR-UPF operates in prediction mode. Indeed, because the inertial sensors used suffer from a large drift in this case, SR-UKF-based importance density is then responsible for shifting the weighted particles toward the high-likelihood regions to improve the accuracy of the vehicle state. The proposed method is compared with some existing estimation methods and the experiment results prove that nGSR-UPF is the most accurate during both outage-free and complete-outage GPS periods.