• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.204-213
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• 2008

In this paper, we present the practical application of an Unscented Kalman Filter (UKF) for an Indoor Mobile Localization System using ultrasonic sensors. It is true that many kinds of localization techniques have been researched for several years in order to contribute to the realization of a ubiquitous system; particularly, such a ubiquitous system needs a high degree of accuracy to be practical and efficient. Unfortunately, a number of localization systems for indoor space do not have sufficient accuracy to establish any special task such as precise position control of a moving target even though they require comparatively high developmental cost. Therefore, we developed an Indoor Mobile Localization System having high localization performance; specifically, the Unscented Kalman Filter is applied for improving the localization accuracy. In addition, we also present the additive filter named 'Pre-filtering' to compensate the performance of the estimation algorithm. Pre-filtering has been developed to overcome negative effects from unexpected external noise so that localization through the Unscented Kalman Filter has come to be stable. Moreover, we tried to demonstrate the performance comparison of the Unscented Kalman Filter and another estimation algorithm, such as the Unscented Particle Filter (UPF), through simulation for our system.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2335-2339
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• 2003

To prevent the critical situation due to the fault in the aircraft sensor system, the fault tolerant system with triple or quadruple redundancy can be made. However, if the faults are occurred in two or more than sensors simultaneously, the conventional fault detection process, such as cross-channel monitoring, may give the wrong fault alarm. For this case, we can detect the fault by estimating the state vector based on the system dynamics model, which is nonlinear for aircraft. In this paper, we propose the unscented Kalman filter to estimate the nonlinear state vector. This filter utilizes the so-called unscented transformation of sigma points featured the statistical characteristics of the random variable. For verification, we perform the simulations for F-16 aircraft with accelerometers, gyros, GPS and air data system.

• 한국지능시스템학회논문지
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• 제15권5호
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• pp.552-559
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• 2005

실시간 순환형 훈련 알고리즘(RTRL)과 같이 경사법에 의해 훈련되는 순환형 뉴럴 네트웍(RNN)은 수렴속도가 매우 느린 단점을 지니고 있다. 이 알고리즘은 또한 오차 역전달 처리과정에서 결코 쉽지 않은 미분 계산을 필요로 한다. 본 논문에서는 완전하게 결합된 RNN의 훈련을 위하여 소위 언센티드 칼만필터라고 불리우는 미분없는 칼만필터 훈련 알고리즘을 시스템의 상태공간 상에서 표현하였다. 미분없는 칼만필터 훈련 알고리즘은 순환형 뉴럴 네트웍 훈련시 미분 계산 없이 매우 빠른 수렴속도와 좋은 추정 성능을 보여준다. 비선형 채널 등화 실험을 통하여 미분 없는 칼만필터 훈련 알고리즘을 이용한 RNN의 성능이 향상되었음을 보였다.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.1-11
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• 2013

The determination of the pose of the imaging camera is a fundamental problem in computer vision. In the monocular case, difficulties in determining the scene scale and the limitation to bearing-only measurements increase the difficulty in estimating camera pose accurately. Many mobile phones now contain inertial measurement devices, which may lend some aid to the task of determining camera pose. In this study, by means of simulation and real-world experimentation, we explore an approach to monocular camera localization that incorporates both observations of the environment and measurements from accelerometers and gyroscopes. The unscented Kalman filter was implemented for this task. Our main contribution is a novel approach to landmark initialization in a Kalman filter; we characterize the tolerance to noise that this approach allows.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2001년도 춘계학술대회 논문집
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• pp.180-189
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• 2001

In this study, recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.

• 제어로봇시스템학회논문지
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• 제13권8호
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• pp.817-823
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• 2007

GPS and INS can be integrated in 3 ways of loose, tight, and ultra-tight configuration. This paper investigates the performance of GPS/INS ultra-tightly integrated system when unscented Kalman filter(UKF) is adopted as well as extended Kalman filter(EKF). Covariance analysis is performed using UFK and EKF for tightly coupled and ultra-tightly coupled systems. Various trajectories such as straight, circle, S-shape, spiral are considered for the simulations of covariance analysis.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2001년도 가을 학술발표회 논문집
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• pp.117-126
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• 2001

In this study, the recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2002년도 추계 학술발표회 논문집
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• pp.213-220
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• 2002

본 연구에서는 최근에 개발된 Unscented Particle Filter (UPF)를 사용한 비선형 동적 구조계의 구조계수 규명기법이 연구되었다. 일반적인 비선형 구조계수 추정 문제의 일반 해는 존재하지 않으나, 그에 대한 대안으로써 선형 근사 기법인 extended Kalman filter (EKF)가 비선형 동적 구조계수의 추정에 주로 사용되어왔다. 그러나, EKF는 구간 선형(piecewise linear) 가정으로 인해 biased estimator이고 비선형성이 상대적으로 높을 때 오차가 큰 추정치를 주는 단점을 가진다. 이를 보완하기 위해서 UPF가 개발되었고, 이 기법은 particle filter의 일종으로써 Unscented Kalman filter (UKF)를 사용하여 importance proposal distribution을 생성한다. 수치실험이 SDOF와 MDOF에 대하여 3가지 경우에 대해서 수행되었다. 비선형 SDOF의 수치 실험으로부터 잡음이 가해진 상태에서 UKF가 EKF에 비해 초기 공분산 행렬의 가정에 대해 정확하고 강인한 추정결과를 보여줌을 보였다 최하층의 column에 비선형 거동이 발생하는 5층 전단 빌딩모형의 수치실험으로부터 UKF가 복잡한 구조물의 구조계수 추정능력이 있음을 보여주었다. 여러 가지 수치실험은 UPF가 EKF보다 비선형 동적 구조계수 추정에 있어서 더 나은 방법임을 보여 주었다.