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dc.contributor.authorFan, Y-
dc.contributor.authorHuang, K-
dc.contributor.authorLiming, D-
dc.identifier.citationApplied Sciences, 2019, 10 (1), pp. 1 - 20en_US
dc.description.abstractIn this study, a nested ensemble filtering (NEF) approach is advanced for uncertainty parameter estimation and uncertainty quantification of a traffic noise model. As an extension of the ensemble Kalman filter (EnKF) and particle filter methods, the proposed NEF method improves upon the ensemble Kalman filter (EnKF) method by incorporating the sample importance resampling (SIR) procedures into the EnKF update process. The NEF method can avoid the overshooting problem (abnormal value (e.g., outside the predefined ranges, complex values) in parameter or state samples) existing in the EnKF update process. The proposed NEF method is applied to the traffic noise prediction on the Trans-Canada Highway in the City of Regina to demonstrate its applicability. The results indicate that: (a) when determining parameters in the traffic noise prediction model, the NEF method provides accurate estimation; (b) the model parameters can be recursively corrected with the NEF method whenever a new measurement becomes available; (c) the uncertainty in the traffic noise model (should be the noise itself) can be well reduced and quantified through the proposed NEF approach.en_US
dc.description.sponsorshipBrunel University Open Access Publishing Funden_US
dc.format.extent1 - 20-
dc.subjectTraffic noiseen_US
dc.subjectEnsemble Kalman filteren_US
dc.subjectParticle filteren_US
dc.titleA Nested Ensemble Filtering Approach for Parameter Estimation and Uncertainty Quantification of Traffic Noise Modelsen_US
dc.relation.isPartOfApplied Sciences-
Appears in Collections:Dept of Mechanical and Aerospace Engineering Research Papers

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