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1887

access icon free Particle filter for nonlinear systems with multiple step randomly delayed measurements

A new particle filter for nonlinear systems with multiple step randomly delayed measurements is proposed. In the proposed method, particles and their weights are updated in the Bayesian estimation framework by considering the multiple step randomly delayed measurement model. Simulation results show that the proposed method has higher estimation accuracy than existing methods.

References

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      • 7. Zhang, Y.G., Huang, Y.L., Zhao, L.: ‘A general framework solution of Gaussian filter with multiple step randomly delayed measurements’, Acta Autom. Sin., 2015, 41, (1), pp. 122135.
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      • 6. Zhang, Y.G., Huang, Y.L., Li, N., et al: ‘Conditional posterior Cramér-Rao lower bound for nonlinear sequential Bayesian estimation with one-step randomly delayed measurements’, Acta Autom. Sin., 2015, 41, (3), pp. 559574.
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http://iet.metastore.ingenta.com/content/journals/10.1049/el.2015.1899
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