Non-uniform-level crossing sampling for efficient sensing of temporally sparse signals

摘要

This study investigates the performance of level-crossing sampling (LCS) for efficient sensing of the temporally sparse random signals assuming that the sampling levels can be chosen arbitrarily. In particular, the authors investigate a non-uniform LCS for sensing temporally sparse signals with sampling levels that are optimised to minimise the reconstruction error based on prior knowledge of signal's probability density function (pdf). Performance results showing the tradeoff between reconstruction error and average number of samples are presented. To define the sampling levels independent from the pdf of the signal, a tractable scheme is proposed. The performance and sampling efficiency of LCS for three schemes of defining the sampling levels when the signal range is known are evaluated.