• Title, Summary, Keyword: 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.

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.

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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Discrete-time robust Kalman filter design in indefinite inner product space

  • Lee, Tae-Hoon;Park, Jin-Bae;Yoon, Tae-Sung;Ra, Won-Sang
    • 제어로봇시스템학회:학술대회논문집
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    • pp.45.2-45
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    • 2002
  • $\textbullet$ Uncertainties are described by sum quadratic constraint(SQC) $\textbullet$ SQC is converted into an indefinite quadratic cost function $\textbullet$ A Kalman filter developed in indefinite inner product space is Krein space Kalman filter $\textbullet$ To minimize the SQC, the Krein space Kalman filter is used $\textbullet$ The proposed robust filter outperforms the standard Kalman filter and existing robust Kalman filter $\textbullet$ The proposed filter has the same recursive, simple structure as the standard Kalman filter $\textbullet$ Easy to design, adequate for on-line implementation

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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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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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Parallel Reduced-Order Square-Root Unscented Kalman Filter for State Estimation of Sensorless Permanent-Magnet Synchronous Motor (센서리스 영구자석 동기전동기의 상태 추정을 위한 병렬 축소 차수 제곱근 무향 칼만 필터)

  • Moon, Cheol;Kwon, Young-Ahn
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.6
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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.

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.

Synchronous Interfusion of the Compensatory Filters Based on Multi-rate Sensors for the Control of the Autonomous Vehicle (자율주행 차량 제어를 위한 다중 주기 센서 기반의 상보 필터 동기 융합)

  • Bak, Jeong-Hyeon;Lee, Kwanghee;Lee, Chul-Hee
    • Transactions of the Korean Society of Automotive Engineers
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    • v.22 no.3
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    • pp.220-227
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    • 2014
  • This paper presents about multi-rate sensors' synchronization and filter fusion via a sigmoid function of the Kalman filter. To synchronize multi-rate sensors, the estimation states of the Kalman filter is modified. A specific matrix that makes the filter choose sensor values only updated is multiplied to measurement matrix. For the filter that has weak points on some criteria, filter fusion is suggested by using sigmoid function. Modified kalman filter is tested with practical case. A sigmoid function was designed for the test and the performance of the modified function is estimated with respect to conventional Kalman filter. Unscented Kalman filter is used to the base filter of the suggested filter because of its stability.

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

  • Park, Sun-Bae;Yoo, Do-Sik
    • The 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.