• 제목/요약/키워드: Kalman filter

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Robust Wavelet Kalman Filter

  • Lee, Taehoon;Park, Jinbae;Taesung Yoon
    • 제어로봇시스템학회:학술대회논문집
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    • pp.39.3-39
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    • 2001
  • Since Kalman filter and wavelet transform techniques are both suitable for a nonstationary process, wavelet-Kalman filter was proposed and applied to various industrial fields. However, the wavelet-Kalman filter subjected to model uncertainty with nonstationary process has not been considered. Thus, the robust wavelet-Kalman filter method is proposed in this paper. The proposed method can prevent the degradation of filter performance when parameter uncertainty exists in both the state and measurement matrices and preserve the merits of the standard Kalman filter in the sense that it produces optimal estimates. A simple example shows that the proposed approach outperforms the standard Kalman filter and the nominal wavelet-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.

Real-time Decision of G/R Ratio using the Dual Kalman Filter (Dual Kalman Filter를 이용한 G/R 비의 실시간 결정)

  • Yoo, Chul-Sang;Kim, Jung-Ho
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.353-356
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    • 2011
  • 본 연구에서는 G/R 비의 실시간 결정을 목적으로 Dual Kalman Filter를 이용하였다. Dual Kalman Filter 는 이중추정(dual estimation)을 기반으로 하는 자료동화기법으로 기존 Kalman Filter와 상이한 상태-공간 모형으로 구성된다. 이에 Dual Kalman Filter와 기존 Kalman Filter의 적용성능을 비교 검토하였으며, 다양한 비교를 위하여 강우의 임계치와 누적시간의 고려여부에 따른 결과를 추가적으로 검토하였다. 두 기법의 적용성능 비교결과 Dual Kalman Filter가 우수한 것으로 나타났다. 이는 Dual Kalman Filter 기법이 G/R 비의 큰 변동성과 이상치를 효과적으로 필터링하고, 시계열 모형의 매개변수를 실시간으로 갱신하여 정확한 예측치를 추정하였기 때문인 것으로 판단된다.

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Kalman Filter-based Navigation Algorithm for Multi-Radio Integrated Navigation System

  • Son, Jae Hoon;Oh, Sang Heon;Hwang, Dong-Hwan
    • Journal of Positioning, Navigation, and Timing
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    • v.9 no.2
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    • pp.99-115
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    • 2020
  • Since GNSS is easily affected by jamming and/or spoofing, alternative navigation systems can be operated as backup system to prepare for outage of GNSS. Alternative navigation systems are being researched over the world, and a multi-radio integrated navigation system using alternative navigation systems such as KNSS, eLoran, Loran-C, DME, VOR has been researched in Korea. Least Square or Kalman filter can be used to estimate navigation parameters in the navigation system. A large number of measurements of the Kalman filter may lead to heavy computational load. The decentralized Kalman filter and the federated Kalman filter were proposed to handle this problem. In this paper, the decentralized Kalman filter and the federated Kalman filter are designed for the multi-radio integrated navigation system and the performance evaluation result are presented. The decentralized Kalman filter and the federated Kalman filter consists of local filters and a master filter. The navigation parameter is estimated by local filters and master filter compensates navigation parameter from the local filters. Characteristics of three Kalman filters for a linear system and nonlinear system are investigated, and the performance evaluation results of the three Kalman filters for multi-radio integrated navigation system are compared.

A Linear Reservoir Model with Kslman Filter in River Basin (Kalman Filter 이론에 의한 하천유역의 선형저수지 모델)

  • 이영화
    • Journal of Environmental Science International
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    • v.3 no.4
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    • pp.349-356
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    • 1994
  • The purpose of this study is to develop a linear reservoir model with Kalman filter using Kalman filter theory which removes a physical uncertainty of :ainfall-runoff process. A linear reservoir model, which is the basic model of Kalman filter, is used to calculate runoff from rainfall in river basin. A linear reservoir model with Kalman filter is composed of a state-space model using a system model and a observation model. The state-vector of system model in linear. The average value of the ordinate of IUH for a linear reservoir model with Kalman filter is used as the initial value of state-vector. A .linear reservoir model with Kalman filter shows better results than those by linear reserevoir model, and decreases a physical uncertainty of rainfall-runoff process in river basin.

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Quadratic Kalman Filter Object Tracking with Moving Pictures (영상 기반의 이차 칼만 필터를 이용한 객체 추적)

  • Park, Sun-Bae;Yoo, Do-Sik
    • Journal of Advanced Navigation Technology
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    • v.20 no.1
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    • pp.53-58
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    • 2016
  • In this paper, we propose a novel quadratic Kalman filter based object tracking algorithm using moving pictures. Quadratic Kalman filter, which is introduced recently, has not yet been applied to the problem of 3-dimensional (3-D) object tracking. Since the mapping of a position in 2-D moving pictures into a 3-D world involves non-linear transformation, appropriate algorithm must be chosen for object tracking. In this situation, the quadratic Kalman filter can achieve better accuracy than extended Kalman filter. Under the same conditions, we compare extended Kalman filter, unscented Kalman filter and sequential importance resampling particle filter together with the proposed scheme. In conculsion, the proposed scheme decreases the divergence rate by half compared with the scheme based on extended Kalman filter and improves the accuracy by about 1% in comparison with the one based on unscented Kalman filter.

Assessment of Backprojection-based FMCW-SAR Image Restoration by Multiple Implementation of Kalman Filter (Kalman Filter 복수 적용을 통한 Backprojection 기반 FMCW-SAR의 영상복원 품질평가)

  • Song, Juyoung;Kim, Duk-jin;Hwang, Ji-hwan;An, Sangho;Kim, Junwoo
    • Korean Journal of Remote Sensing
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    • v.37 no.5_3
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    • pp.1349-1359
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    • 2021
  • Acquisition of precise position and velocity information of GNSS-INS (Global Navigation Satellite System; Inertial Navigation System) sensors in obtaining SAR SLC (Single Look Complex) images from raw data using BPA (Backprojection Algorithm) was regarded decisive. Several studies on BPA were accompanied by Kalman Filter for sensor noise oppression, but often implemented once where insufficient information was given to determine whether the filtering was effectively applied. Multiple operation of Kalman Filter on GNSS-INS sensor was presented in order to assess the effective order of sensor noise calibration. FMCW (Frequency Modulated Continuous Wave)-SAR raw data was collected from twice airborne experiments whose GNSS-INS information was practically and repeatedly filtered via Kalman Filter. It was driven that the FMCW-SAR raw data with diverse path information could derive different order of Kalman Filter with optimum operation of BPA image restoration.

Observer design with Gershgorin's disc

  • Si, Chen;Zhai, Yujia
    • Journal of the Korea Convergence Society
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    • v.4 no.4
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    • pp.41-48
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    • 2013
  • Observer design for system with unknown input was carried out. First, Kalman filter was considered to estimate system state with White noise. With the results of Kalman filter design, state observer, controller properties, including controllability and observability, and the Kalman filter structure and algorithm were also studied. Kalman filter algorithm was applied to Position and velocity measurement based on Kalman filter with white noise, and it was constructed and achieved by programming based on Matlab programming. Finally, observer for system with unknown input was constructed with the help of Gershgorin's disc theorem. With the designed observer, system states was constructed and applied to system with unknown input. By simulation results, estimation performance was verified. In this project, state feedback control theory, observer theory and relevant design procedure, as well as Kalman filter design were understood and used in practical application.

Centralized Kalman Filter with Adaptive Measurement Fusion: its Application to a GPS/SDINS Integration System with an Additional Sensor

  • Lee, Tae-Gyoo
    • International Journal of Control, Automation, and Systems
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    • v.1 no.4
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    • pp.444-452
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    • 2003
  • An integration system with multi-measurement sets can be realized via combined application of a centralized and federated Kalman filter. It is difficult for the centralized Kalman filter to remove a failed sensor in comparison with the federated Kalman filter. All varieties of Kalman filters monitor innovation sequence (residual) for detection and isolation of a failed sensor. The innovation sequence, which is selected as an indicator of real time estimation error plays an important role in adaptive mechanism design. In this study, the centralized Kalman filter with adaptive measurement fusion is introduced by means of innovation sequence. The objectives of adaptive measurement fusion are automatic isolation and recovery of some sensor failures as well as inherent monitoring capability. The proposed adaptive filter is applied to the GPS/SDINS integration system with an additional sensor. Simulation studies attest that the proposed adaptive scheme is effective for isolation and recovery of immediate sensor failures.

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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