Multi-sensor optimal information fusion Kalman filters with applications

Sun, Shuli

doi:10.1016/j.ast.2003.08.003

Cited by 165 publications

(69 citation statements)

References 5 publications

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“…However as we aforementioned, due to the big frequency difference between u and v, the normal Kalman 1 Robocup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. 2 The parameters used for the proposed method are: ≈ 31.25) state-updates during the sampling period of u before every measurement-update, this discontinuity cannot be elimiated by filter tuning.…”

Section: A Methods Validationmentioning

confidence: 99%

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Filling the gap between low frequency measurements with their estimates

Wang

Kostic

Jansen³

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-The use of redundant sensors brings a rich diversity of information, nevertheless fusing different sensors that run at vastly different frequencies into a proper estimate is still a challenging sensor fusion problem. Instead of using the size-varying measurements and thereby the size-varying filters during each sampling period, we propose to find a substitute of the unavailable low frequency measurements such that we can avoid using different sampling frequencies in one filter. In the gap between the sampling of two low frequency measurements, the use of these substitutes produces smoother estimates. In both the proof of concept simulation and the localization experiment performed on an indoor soccer robot, our proposed approach exhibits an improved performance compared to the size-varying Kalman filter methods.

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Section: A Methods Validationmentioning

confidence: 99%

“…When the sampling frequencies of different sources of measurements are consistent, we can analyze the cross correlation and apply different standard techniques, e.g. [1] and [2]. However when the sensors operate at different frequencies, the availability of the measurements is timevarying.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Filling the gap between low frequency measurements with their estimates

Wang

Kostic

Jansen³

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…Other related publications cited in the Table 2.7 are [338]- [339], [337], [332], [333,334], [37], [46], [47], [48], [48], [60], [59,58], [57], [56], [55], [54], [53], [52]. Other related publications cited in the Table 2.8 are [67], [68], [91], [93,94], [95], [115], [116], [117], [124], [125], [127] and [128]. Other related publications cited in the Table 2.9 are [131], [169,170], [170,171,172], [183], [187], [200], [240], [241], [244], …”

Section: Msdf Systemsmentioning

confidence: 99%

Distributed Kalman filtering

Mahmoud¹

2012

Distributed Control and Filtering for Industrial Systems

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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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Multi-sensor optimal information fusion Kalman filters with applications

Cited by 165 publications

References 5 publications

Filling the gap between low frequency measurements with their estimates

Filling the gap between low frequency measurements with their estimates

Distributed Kalman filtering

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

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