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- W3006437443 abstract "For Gaussian approximation recursive filter (GASF) framework, the same information is used for the correction at two adjacent epochs. To reduce the complexity of processing and ensure the accuracy of the algorithm, in this paper, for nonlinear system with randomly delayed measurements and synchronizttion correlated noises, we present a fast federated Kalman filtering algorithm, where an innovation technique is used. To deal with multi-sensor measurements, a new coefficient allocation method is used to reconstruct the fusion innovation sequence. Then, a federated filtering algorithm is derived based on the optimal information sharing coefficient. Finally, we state the superiority of the algorithm in this paper." @default.
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- W3006437443 date "2019-11-01" @default.
- W3006437443 modified "2023-09-22" @default.
- W3006437443 title "Fast Federated Kalman Filter for Nonlinear Systems with Randomly Delayed Measurements and Correlated Noises" @default.
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- W3006437443 doi "https://doi.org/10.1109/cac48633.2019.8996658" @default.
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