Bandwidth Estimation From Multiple Level-Crossings of Stochastic Signals

Motivated by a broad range of potential applications in event-driven signal processing inference, we examine a class of spectral moments estimators derived from multiple level-crossings of a stationary Gaussian stochastic signal as well as non-Gaussian signals being monotone transformations of the Gaussian signal. The established spectral moments estimators are used to obtain estimates of the stochastic signal bandwidth. Both the mean-squared bandwidth and the absolute bandwidth are taken into account. Also the notion of the power bandwidth is introduced and its estimation is examined. We prove that the proposed estimates are consistent as the length of the observed time interval is increasing. The accuracy of the proposed bandwidth estimation methods from multiple level-crossings is assessed in simulation studies.