Abstract
Pseudoperiodic signals are commonly observed in many real applications. Identifying the period is crucial before analyzing the signals for such applications as monitoring, detection, and diagnosis. However, attention paid to period estimation of such signals is limited due to two contradictory challenges, arising from high-level period fluctuations and strong background noises, although numerous methods have been developed for periodic signals during the past decades. A novel method, which extends the noise-resistant correlation (NRC) method to pseudoperiodic signals and is called the extended NRC (ExNRC) method, has been developed in this article to solve the two challenges simultaneously. The ExNRC method can achieve a balance between feature enhancement and noise suppression by tuning a free parameter. Therefore, this method can be applied to pseudoperiodic signals contaminated with high-level period fluctuations or strong background noises. Properties of the ExNRC method are analyzed, and its performance is compared with conventional methods via simulation studies and real applications.
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