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• W2968162583 abstract "To settle the problem of distributed nonlinear state estimation in sensor networks with naive nodes, a novel distributed high-degree cubature information filter with embedded hybrid consensus (DHCIF) is proposed. The multi-dimensional Gaussian weighted integrals involved in the filtering process are approximated by the fifth-degree cubature rule. A novel scheme, with consideration for the predicted measurement errors, is applied to compute the information contribution. With parallel consensus performed on both prior and measurement information, the proposed DHCIF is derived. The stability analysis with regard to consistency and boundedness of estimation errors for the proposed DHCIF is also developed. Finally, the effectiveness and advantage of the proposed DHCIF is validated by a typical maneuvering target tracing scenario. The experimental results indicate that the proposed DHCIF outperforms the existing algorithms in the aspects of estimation accuracy, consistency and consensus at the cost of a little more extra computation burden." @default.
• W2968162583 created "2019-08-22" @default.
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• W2968162583 date "2019-01-01" @default.
• W2968162583 modified "2023-10-14" @default.
• W2968162583 title "Distributed High-Degree Cubature Information Filter With Embedded Hybrid Consensus" @default.
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• W2968162583 doi "https://doi.org/10.1109/access.2019.2933565" @default.
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