Inverse modeling and joint state-parameter estimation with a noise mapping meta-model
| Autor: | Vivien Mallet, Arnaud Can, Antoine Lesieur, Pierre Aumond |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010504 meteorology & atmospheric sciences
Acoustics and Ultrasonics Mean squared error Computer science Noise map Function (mathematics) Best linear unbiased prediction 01 natural sciences Reduction (complexity) Noise Data assimilation Arts and Humanities (miscellaneous) 0103 physical sciences A priori and a posteriori 010301 acoustics Algorithm 0105 earth and related environmental sciences |
| Zdroj: | The Journal of the Acoustical Society of America. 149(6) |
| ISSN: | 1520-8524 |
| Popis: | This study aims to produce dynamic noise maps based on a noise model and acoustic measurements. To do so, inverse modeling and joint state-parameter methods are proposed. These methods estimate the input parameters that optimize a given cost function calculated with the resulting noise map and the noise observations. The accuracy of these two methods is compared with a noise map generated with a meta-model and with a classical data assimilation method called best linear unbiased estimator. The accuracy of the data assimilation processes is evaluated using a “leave-one-out” cross-validation method. The most accurate noise map is generated computing a joint state-parameter estimation algorithm without a priori knowledge about traffic and weather and shows a reduction of approximately 26% in the root mean square error from 3.5 to 2.6 dB compared to the reference meta-model noise map with 16 microphones over an area of 3 km2. |
| Databáze: | OpenAIRE |
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