• Title, Summary, Keyword: Extended Kalman Filter

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Performance Comparison of Various Extended Kalman Filter and Cost-Reference Particle Filter for Target Tracking with Unknown Noise (노이즈 불확실성하에서의 확장칼만필터의 변종들과 코스트 레퍼런스 파티클필터를 이용한 표적추적 성능비교)

  • Shin, Myoungin;Hong, Wooyoung
    • Journal of the Korea Society for Simulation
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    • v.27 no.3
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    • pp.99-107
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    • 2018
  • In this paper, we study target tracking in two dimensional space using a Extended Kalman filter(EKF), various Extended Kalman Filter and Cost-Reference Particle Filter(CRPF), which can effectively estimate the state values of nonlinear measurement equation. We introduce various Extended Kalman Filter which the Unscented Kalman Filter(UKF), the Central Difference Kalman Filter(CDKF), the Square Root Unscented Kalman Filter(SR-UKF), and the Central Difference Kalman Filter(SR-CDKF). In this study, we calculate Mean Square Error(MSE) of each filters using Monte-Carlo simulation with unknown noise statistics. Simulation results show that among the various of Extended Kalman filter, Square Root Central Difference Kalman Filter has the best results in terms of speed and performance. And, the Cost-Reference Particle Filter has an advantageous feature that it does not need to know the noise distribution differently from Extended Kalman Filter, and the simulation result shows that the excellent in term of processing speed and accuracy.

Nonlinearity-Compensation Extended Kalman Filter for Handling Unexpected Measurement Uncertainty in Process Tomography

  • Kim, Jeong-Hoon;Ijaz, Umer Zeeshan;Kim, Bong-Seok;Kim, Min-Chan;Kim, Sin;Kim, Kyung-Youn
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1897-1902
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    • 2005
  • The objective of this paper is to estimate the concentration distribution in flow field inside the pipeline based on electrical impedance tomography. Special emphasis is given to the development of dynamic imaging technique for two-phase field undergoing a rapid transient change. Nonlinearity-compensation extended Kalman filter is employed to cope with unexpected measurement uncertainty. The nonlinearity-compensation extended Kalman filter compensates for the influence of measurement uncertainty and solves the instability of extended Kalman filter. Extensive computer simulations are carried out to show that nonlinearity-compensation extended Kalman filter has enhanced estimation performance especially in the unexpected measurement environment.

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Aircraft parameter estimation using the extended kalman filter (확장 칼만 필터를 이용한 항공기 파라미터 추정)

  • 송용규;황명신;박욱제
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1655-1658
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    • 1997
  • To obtain aircraft dynamic parameters, various estimation methods such as Maximum Likelihood, Linear Regression are applied. In this paper we adopt the extended Kalman filter(EKF) to estimate the stability and control derivatives in aircraft dynamic models from flight test data. The extended Kalman filter is applied to nonlinear augmented system assuming that unknown parameters are additional states. In this work, the results of the extended Kalman filter are compared with the results of the wind tunnel test using Chang Gong-91 aircraft flight test data.

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Performance Analysis of Adaptive Extended Kalman Filter in Tracking Radar (추적 레이더에서 적응형 확장 칼만 필터의 성능 분석)

  • Song, Seungeon;Shin, Han-Seop;Kim, Dae-Oh;Ko, Seokjun
    • IEMEK Journal of Embedded Systems and Applications
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    • v.12 no.4
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    • pp.223-229
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    • 2017
  • An angle error is a factor obstructing to track accurate position in tracking radars. And the noise incurring the angle error can be divided as follows; thermal noise and glint. In general, Extended Kalman filter used in tracking radars is designed with considering thermal noise only. The Extended Klaman filter uses a fixed measurement error covariance when updating an estimate state by using ahead state and measurement. But, a noise power varies according to the range. Therefore we purposes the adaptive Kalman filter which changes the measurement noise covariance according to the range. In this paper, we compare the performance of the Extended Kalman filter and the proposed adaptive Kalman filter by considering KSLV-I (Korean Satellite Launch Vehicles).

Nonlinear Filter for Orbit Determination (궤도결정을 위한 비선형 필터)

  • Yoon, Jangho
    • Journal of Aerospace System Engineering
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    • v.10 no.1
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    • pp.21-28
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    • 2016
  • Orbit determination problems have been interest of many researchers for long time. Due to the high nonlinearity of the equation of motion and the measurement model, it is necessary to linearize the both equations. To avoid linearization, the filter based on Fokker-Planck equation is designed. with the extended Kalman filter update mechanism, in which the associated Fokker-Planck equation was solved efficiently and accurately via discrete quadrature and the measurement update was done through the extended Kalman filter update mechanism. This filter based on the DQMOM and the EKF update is applied to the orbit determination problem with appropriate modification to mitigate the filter smugness. Unlike the extended Kalman filter, the hybrid filter based on the DQMOM and the EKF update does not require the burdensome evaluation of the Jacobian matrix and Gaussian assumption for the system, and can still provide more accurate estimations of the state than those of the extended Kalman filter especially when measurements are sparse. Simulation results indicate that the advantages of the hybrid filter based on the DQMOM and the EKF update make it a promising alternative to the extended Kalman filter for orbit estimation problems.

A MODIFIED EXTENDED KALMAN FILTER METHOD FOR MULTI-LAYERED NEURAL NETWORK TRAINING

  • KIM, KYUNGSUP;WON, YOOJAE
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.22 no.2
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    • pp.115-123
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    • 2018
  • This paper discusses extended Kalman filter method for solving learning problems of multilayered neural networks. A lot of learning algorithms for deep layered network are sincerely suffered from complex computation and slow convergence because of a very large number of free parameters. We consider an efficient learning algorithm for deep neural network. Extended Kalman filter method is applied to parameter estimation of neural network to improve convergence and computation complexity. We discuss how an efficient algorithm should be developed for neural network learning by using Extended Kalman filter.

Performance of PN Code Synchronization with Extended Kalman Filter for a Direct-Sequence Spread-Spectrum System (직접시퀀스 확산대역 시스템을 위한 Extended Kalman Filter 기반의 PN 부호 동기화 성능)

  • Kim, Jin-Young;Yang, Jae-Soo
    • Journal of The Institute of Information and Telecommunication Facilities Engineering
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    • v.8 no.3
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    • pp.107-110
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    • 2009
  • In this paper, a PN code tracking loop with extended Kalman filter (EKF) is proposed for a direct-sequence spread-spectrum. EKF is used to estimate amplitude and delay in a multipath. fading channel. It is shown that tracking error performance is significantly improved by EKF compared with a conventional tracking loop.

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Performance of PN Tracking with Extended Kalman Filter (Extended Kalman Filter기반의 PN부호 추적성능)

  • Bae, Jung-Nam;Koo, Sung-Wan;Kim, Sung-Ill;Kim, Jin-Young
    • 한국정보통신설비학회:학술대회논문집
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    • pp.112-114
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    • 2009
  • In this paper, a PN code tracking loop with extended Kalman filter (EKF) is proposed for a direct-sequence spread-spectrum. EKF is used to estimate amplitude and delay in a multipath fading channel. It is shown that tracking error performance is significantly improved by EKF compared with a conventional tracking loop.

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A Krein Space Approach for Robust Extended Kalman Filtering on Mobile Robots in the Presence of Uncertainties

  • Jin, Seung-Hee;Park, Jin-Bae
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1771-1776
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    • 2003
  • In mobile robot navigation, one of the key problems is the pose estimation of the mobile robot. Although the odometry can be used to describe the motions of the mobile robots quite simple and accurately, the validities of the models are limited by a number of error sources contaminating the encoder outputs so that applying the conventional extended Kalman filter to these nominal model does not yield the satisfactory performance. As a remedy for this problem, we consider the uncertain nonlinear kinematic model of the mobile robot that contains the norm bounded uncertainties and also propose a new robust extended Kalman filter based on the Krein space approach. The proposed robust filter has the same recursive structure as the conventional extended Kalman filter and can hence be readily designed to effectively account for the uncertainties. The computer simulations will be given to verify the robustness against the parameter variation as well as the reliable performance of the proposed robust filter.

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Extended Kalman Filter Based GF-INS Angular Velocity Estimation Algorithm

  • Kim, Heyone;Lee, Junhak;Oh, Sang Heon;Hwang, Dong-Hwan;Lee, Sang Jeong
    • Journal of Positioning, Navigation, and Timing
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    • v.8 no.3
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    • pp.107-117
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    • 2019
  • When a vehicle moves with a high rotation rate, it is not easy to measure the angular velocity using an off-the-shelf gyroscope. If the angular velocity is estimated using the extended Kalman filter in the gyro-free inertial navigation system, the effect of the accelerometer error and initial angular velocity error can be reduced. In this paper, in order to improve the navigation performance of the gyro-free inertial navigation system, an angular velocity estimation method is proposed based on an extended Kalman filter with an accelerometer random bias error model. In order to show the validity of the proposed estimation method, angular velocities and navigation outputs of a vehicle with 3 rev/s rotation rate are estimated. The results are compared with estimates by other methods such as the integration and an extended Kalman filter without an accelerometer random bias error model. The proposed method gives better estimation results than other methods.