• Journal of Positioning, Navigation, and Timing
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• 제10권2호
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• pp.83-89
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• 2021

The extended Kalman filter (EKF) is widely used for global navigation satellite system (GNSS) applications. It is difficult to obtain precise positions with an EKF one-way (forward or backward) filter. In this paper, we propose an EKF smoother to improve the positioning accuracy by integrating forward and backward filters. For the EKF smoother experiment, we performed PPP using GNSS data received at the DAEJ reference station for a month. The effectiveness of the proposed approach is validated with multi-GNSS kinematic PPP experiments. The EKF smoother showed 35%, 6%, and 22% improvement in east, north, and up directions, respectively. In addition, accurate tropospheric zenith total delay (ZTD) values were calculated by a smoother. Therefore, the results from EKF smoother demonstrate that better accuracy of position can be achieved.

• 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.

• 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.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권7_8호
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• pp.744-751
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• 2003

본 논문에서는 최근 뛰어난 예측력으로 각광받는 최소제곱 Support Vector Machine(Least Square Support Vector Machine: LS-SVM)과 First Principle(FP)을 결합한 하이브리드 최소제곱ㆍSupport Vector Machine 모델, HLS-SVM(Hybrid Least Square-Super Vector Machine)을 제안한다. 제안한 모델인 하이브리드 최소제곱 Support Vector Machine을 기존의 방법인 하이브리드 신경망(Hybrid Neural Network:HNN), 비선형 칼만필터와 하이브리드 신경망을 결합한 HNN-EKF (Hybrid Neural Network with Extended Kalman Filter) 모델과 비교해 보았다. HLS-SVM 모델은 학습 및 validation 과정에서는 HNN-EKF와 근사한 성능을 보였고, HNN 보다는 우수한 결과를 보였고, 일반화 성능에서는 HNN-EKF에 비해 3배, HNN보다 100배정도 우수한 결과를 보였다.

• Journal of Information Technology Applications and Management
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• 제15권2호
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• pp.51-65
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• 2008

The purpose of this paper is introducing WiFi based EKF(Extended Kalman Filter) method for indoor positioning. The advantages of our EKF method include: 1) Any special equipment dedicated for positioning is not required. 2) implementation of EKF does not require off-line phase of fingerprinting methods. 3) The EKF effectively minimizes squared deviation of the trilateration method. In order to experimentally prove the advantages of our method, we implemented indoor positioning systems making use of the K-NN(K Nearest Neighbors), Bayesian, decision tree, trilateration, and our EKF methods. Our experimental results show that the average-errors of K-NN, Bayesian and decision tree methods are all close to 2.4 meters whereas the average errors of trilateration and EKF are 4.07 meters and 3.528 meters, respectively. That is, the accuracy of our EKF is a bit inferior to those of fingerprinting methods. Even so, our EKF is accurate enough to be used for practical indoor LBS systems. Moreover, our EKF is easier to implement than fingerprinting methods because it does not require off-line phase.

• 로봇학회논문지
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• 제5권4호
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• pp.367-376
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• 2010

This Paper Proposes A Robust Image Stabilization System For A Mobile Robot Using An Extended Kalman Filter (Ekf). Though Image Information Is One Of The Most Efficient Data Used For Robot Navigation, It Is Subjected To Noise Which Is The Result Of Internal Vibration As Well As External Factors Such As Uneven Terrain, Stairs, Or Marshy Surfaces. The Camera Vibration Deteriorates The Image Resolution By Destroying The Image Sharpness, Which Seriously Prevents Mobile Robots From Recognizing Their Environment For Navigation. In This Paper, An Inclinometer Was Used To Measure The Vibration Angle Of The Camera System Mounted On The Robot To Obtain A Reliable Image By Compensating For The Angle Of The Camera Vibration. In Addition The Angle Prediction Obtained By Using The Ekf Enhances The Image Response Analysis For Real Time Performance. The Experimental Results Show The Effectiveness Of The Proposed System Used To Compensate For The Blurring Of The Images.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.600-608
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• 2014

This paper presents estimation of d-axis and q-axis inductance of an interior permanent magnet synchronous motor (IPMSM) by using an extended Kalman filter (EKF). The EKF is widely used for control applications including the motor sensorless control and parameter estimation. The motor parameters can be changed by temperature and air-gap flux. In particular, the variation of the inductance affects torque characteristics like the maximum torque per ampere (MTPA) control. Therefore, by estimating the parameters, it is possible to improve the torque characteristics of the motor. The performance of the proposed estimator is verified by simulations and experimental results based on an 11kW PMSM drive system.

• 한국지능시스템학회논문지
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• 제23권5호
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• pp.418-422
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• 2013

본 논문은 비가우시안 노이즈가 존재하는 수중환경에서 비선형 필터 기법에 따른 2차원 위치 추정에 관한 연구 내용이다. 최근 위치 추정을 위한 필터로 확장형 칼만필터(EKF: Extended Kalman filter)가 많이 사용되고 있다. 하지만, 수중과 같은 비가우시안 노이즈가 존재하는 비선형 시스템에서는 많은 문제점을 가지고 있다. 따라서 본 논문에서는 상태변이의 예측을 기반으로한 EKF를 대신하여 통계적 발생인자 에 기반을 둔 분포 재해석 기법을 이용한 2차원 파티클필터 (TDPF: Two-Dimension Particle Filter)를 제안한다. 모의 실험을 통하여 Non-Gaussian Noise 가 존재하는 수중환경에서 제안하는 TDPF의 성능을 EKF와 비교분석하였으며 TDPF가 EKF보다 정확한 위치 추정결과를 제공하는 것을 확인하였다.

• 한국통신학회논문지
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• 제37A권11호
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• pp.1001-1011
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• 2012

본 논문에서는 IEEE 802.11 시스템에서 경쟁 중인 터미널 수를 추정하고 이를 반영할 때 시스템 성능에 미치는 영향을 분석한다. IEEE 802.11 시스템에서는 터미널간의 다중 접근의 방법으로 DCF (Distributed Coordination Function)를 이용하고 있으며 경쟁하는 터미널 수를 정확하게 추정하여 반영하는 것이 시스템 throughput 증가하는데 중요한 요소가 된다. 본 논문에서는 터미널 수를 추정하는 방법으로 노이즈 정보가 필요하지 않는 Extended H Infinity Filter (EHIF)를 이용하여 터미널 수를 추정하는 방법을 제안한다. 경쟁하는 터미널의 수가 saturated되는 경우와 non-saturated되는 네트워크 환경에서 EHIF가 기존의 Extended Kalman Filter (EKF) 방법보다 좋은 성능을 가짐을 모의실험을 통해 확인하였고 이를 정량적으로 분석하였다.

• 제어로봇시스템학회논문지
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• 제12권8호
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• pp.780-788
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• 2006

This paper deals with INS/GPS tightly coupled integration algorithms using extend Kalman filter (EKF) and unscented Kalman filter (UKF). In the tightly coupled approach, nonlinear pseudorange measurement models are used for the INS/GPS integration Kalman filter. Usually, an EKF is applied for this task, but it may diverge due to poor functional linearization of the nonlinear measurement. The UKF approximates a distribution about the mean using a set of calculated sigma points and achieves an accurate approximation to at least second-order. We introduce the generalized scaled unscented transformation which modifies the sigma points themselves rather than the nonlinear transformation. The generalized scaled method is used to transform the pseudo range measurement of the tightly coupled approach. To compare the performance of the EKF- and UKF-based tightly coupled approach, real van test and simulation have been carried out with feedforward and feedback indirect Kalman filter forms. The results show that the UKF and EKF have an identical performance in case of the feedback filter form, but the superiority of the UKF is demonstrated in case of the feedforward filer form.