Discrete wavelet transform on uncertain data: Efficient online implementation for practical applications

Abstract

The discrete wavelet transform has a huge number of applications in science and engineering. It allows to extract features and obtain information often hidden in raw signals. For applications in metrology the propagation of uncertainties for this transform is relevant. By using and extending existing techniques for propagating uncertainties in the context of discrete filtering, we propose an efficient implementation of the discrete wavelet transform that enables the online propagation of uncertainties. The proposed approach is illustrated by its application to simulated data and to data from an experimental testbed.