New approach to information fusion steady-state Kalman filtering

Deng, Zili; Gao, Yuan; Mao, Lin; Li, Yun; Hao, Gang

doi:10.1016/j.automatica.2005.04.020

Cited by 229 publications

(173 citation statements)

References 15 publications

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“…Kim proposed the dynamic Kalman filter which robustly tracks a ball in the dynamic condition by controlling the velocity of the state vector [8]. Based on the ARMA innovation model and Lyapunov equations, Deng et al [9] presented an approach to handle the information fusion filtering, prediction, and smoothing problems for the state and signal. Ding et al [10] and Geng and Wang [11] proposed the adaptive Kalman methods to tune the Q matrix to the optimal magnitude automatically.…”

Section: Introductionmentioning

confidence: 99%

Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding

Liu

Yung

et al. 2013

Advances in Mechanical Engineering

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In order to manufacture high quality microproducts, the precision control of injected plastic melt in the injection chamber during a microinjection process requires real-time tracking of the melt pressure when the melt passes through the nozzle. A novel type of adaptive Kalman filter algorithm based on F-distribution is proposed in this paper. This adaptive Kalman filter can switch the system between the steady state and transient state by comparing the differences of input data in F-distribution. By resetting the Kalman gain and other relevant parameters, the adaptive function guarantees the convergence of the filtered signal during the tracking process and tracks the moments which sudden changes occur in the pressure signature. The simulation experiment results show that the method can reduce the effect of measurement noise more quickly and effectively. The method is proven to be effective for microinjection molding applications.

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Section: Introductionmentioning

confidence: 99%

Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding

Liu

Yung

et al. 2013

Advances in Mechanical Engineering

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“…Unified fusion rules for the optimal linear estimation fusion and several distributed weighting state fusers were presented in [2][3][4][5], where the three distributed weighting fusers have the accuracy relations: the accuracy of the fuser weighted by matrices is higher than that of the fuser weighted by scalars, and the accuracy of the fuser weighted by diagonal matrices is between of them. However, all of the above weighting fusers have the limitation that in order to compute the optimal weights, the computation of the cross-covariances between the local estimation errors is required, while the cross-covariances are usually unknown [6] or their computation is very complex [7] in many applications.…”

Section: Introductionmentioning

confidence: 99%

Distributed Weighting Fusion and Covariance Intersection Fusion Kalman Smoother for Systems with Colored Measurement Noises

Zhang¹,

Qi²

2016

Proceedings of the 2016 International Conference on Intelligent Control and Computer Application

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Abstract-For two-sensor system with colored measurement noises, based on the classical Kalman filtering method, distributed weighting fusion method and the covariance intersection (CI) fusion method, a CI fusion steady-state Kalman smoother is presented, which is independent of the unknown cross-covariance. It is difficult or complex to compute the crosscovariance in most cases. The accuracy comparison of the CI fusion Kalman smoother with three classical distributed weighting fusion Kalman smoothers which are weighted respectively by matrices, diagonal matrices, and scalars is proven, i.e., its accuracy is higher than that of each local Kalman smoother, and lower than that of optimal fuser weighted by matrices with known cross-covariance. The formula of the actual smoothing error variance of the CI fuser is given and the geometric interpretation of the above accuracy relations is presented based on the covariance ellipses. The Monte-Carlo simulation results show its effectiveness and correctness.

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“…In [31], consensus strategies of DKF are discussed where the problem of estimating the state of a dynamical system from distributed noisy measurements is considered with the help of a two-stage strategy for estimation. Other DKF methods and their applications can be seen in [7], [8], [9], [10], [101], [151], [152], [158], [162], [202], [203], [204], [205], [206], [276], [297], [298] and [300].…”

Section: Introductionmentioning

confidence: 99%

Distributed Kalman Filtering

Khalid¹

2014

Networked Filtering and Fusion in Wireless Sensor Networks

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New approach to information fusion steady-state Kalman filtering

Cited by 229 publications

References 15 publications

Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding

Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding

Distributed Weighting Fusion and Covariance Intersection Fusion Kalman Smoother for Systems with Colored Measurement Noises

Distributed Kalman Filtering

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