2014
DOI: 10.1016/j.sigpro.2013.12.013
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Robust weighted fusion Kalman filters for multisensor time-varying systems with uncertain noise variances

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Cited by 126 publications
(153 citation statements)
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“…According to the method given in literature [8], the Kalman filter is designed to estimate the torsion angle based on the measured motor speed and wheel speed. The estimated formula is shown in (13).…”
Section: A Kalman Filtermentioning
confidence: 99%
“…According to the method given in literature [8], the Kalman filter is designed to estimate the torsion angle based on the measured motor speed and wheel speed. The estimated formula is shown in (13).…”
Section: A Kalman Filtermentioning
confidence: 99%
“…The resource-constrained networked systems are influenced by noise disturbances, which are generated by the communication facilities and geographical 15 location of sensor nodes. When the parameter uncertainties exist in the systems, the estimation performance would be deteriorated.…”
Section: Introductionmentioning
confidence: 99%
“…The cross-correlation between the measurement noise and process noise are discussed in [12,13,14]. For noise sequences with uncertain variances, the actual filtering error variances [15,16,17] are obtained with a minimal upper bound for all admissible uncertainties. However, the generation mechanism of 30 multi-step cross-correlation for noises is not clearly analyzed.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, for a system with uncertain noise variances, according to the minimax robust estimation principle, 4 based on the worst-case conservative system with conservative upper bounds of uncertain noise variances, the robust local and fused Kalman filters, 5 predictors, 6 smoothers, 7 and white noise deconvolution smoothers 8 have been presented by the Lyapunov equation approach, respectively, in the sense that the actual estimation error variances of each estimator are guaranteed to have a minimal upper bound for all the admissible uncertainties. However, only the uncertain noise variances were considered in previous works, [5][6][7][8] whereas the model parameters of the considered system were all assumed to be exactly known and the correlated noises were not considered in other works. [2][3][4][5][6][7][8] However, in many engineering applications, the correlated noises often arise.…”
Section: Introductionmentioning
confidence: 99%