This work underlines the ability of the discrete wavelet transform to recover sensor signals subjected to drift effects. The drift resides in low frequencies. so that it is needed to reveal the signal trend. So far, discrete wavelet transform (DWT) is an efficient tool for pre-processing drifting sensor responses as this technique provides a multi-scale processing analysis where the signal is split into low- and high-frequency components at different scales (or different frequency bands) with different resolutions. The trend is the slowest part of the signal and as the scale increases a better estimate of the unknown trend is obtained. Once the signal components, where drift contamination is present, are selected and discarded, the pre-processed signal is not distorted by excessive cutting off low-frequency components. The results are compared with ones obtained by applying standard high-pass filters.
Recovery of drifting sensor responses by means of DWT analysis
Distante C;Siciliano P
2007
Abstract
This work underlines the ability of the discrete wavelet transform to recover sensor signals subjected to drift effects. The drift resides in low frequencies. so that it is needed to reveal the signal trend. So far, discrete wavelet transform (DWT) is an efficient tool for pre-processing drifting sensor responses as this technique provides a multi-scale processing analysis where the signal is split into low- and high-frequency components at different scales (or different frequency bands) with different resolutions. The trend is the slowest part of the signal and as the scale increases a better estimate of the unknown trend is obtained. Once the signal components, where drift contamination is present, are selected and discarded, the pre-processed signal is not distorted by excessive cutting off low-frequency components. The results are compared with ones obtained by applying standard high-pass filters.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
