In this work we evaluate the mean statistical errors incurred within the real-time noise mapping of Dynamap for the equivalent noise levels of each acoustic map. In order to estimate the total error we rely on the Central Limit Theorem which asserts that for the sum of N uncorrelated random processes the total variance of the sum, Ï2N, is given by the sum of the individual variances. In the present case, we have identified four processes whose fluctuations contribute to the total variance (error), which denote as Ï2T, yielding the result Ï2T== Ï2pred+ Ï2state+ Ï2comp+ Ï2sample, corresponding to the intrinsic prediction error of the method, Ï2pred, the statistical variance of the equivalent noise levels measured by the monitoring stations, Ï2stat, the different cluster compositions for different time intervals, Ï2comp, and the variance due to stratified sampling, Ï2sample. The overall mean error is expected to be bounded by about (1-3) dB.
Benocci, R., Roman, H., Smiraglia, M., Zambon, G. (2017). Error analysis of real-time acoustic maps for Dynamap. In INTER-NOISE 2017 - 46th International Congress and Exposition on Noise Control Engineering: Taming Noise and Moving Quiet. Institute of Noise Control Engineering.
Error analysis of real-time acoustic maps for Dynamap
Benocci, R;Roman, HE;Smiraglia, M;Zambon, G
2017
Abstract
In this work we evaluate the mean statistical errors incurred within the real-time noise mapping of Dynamap for the equivalent noise levels of each acoustic map. In order to estimate the total error we rely on the Central Limit Theorem which asserts that for the sum of N uncorrelated random processes the total variance of the sum, Ï2N, is given by the sum of the individual variances. In the present case, we have identified four processes whose fluctuations contribute to the total variance (error), which denote as Ï2T, yielding the result Ï2T== Ï2pred+ Ï2state+ Ï2comp+ Ï2sample, corresponding to the intrinsic prediction error of the method, Ï2pred, the statistical variance of the equivalent noise levels measured by the monitoring stations, Ï2stat, the different cluster compositions for different time intervals, Ï2comp, and the variance due to stratified sampling, Ï2sample. The overall mean error is expected to be bounded by about (1-3) dB.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.