Research Information System
26th IEEE Signal Processing and Communications Applications Conference (SIU), İzmir, Turkey, 2 - 05 May 2018
Period determination is a challenging process, if the periodic-like signal is contaminated with a noise or noisy interference signals. Previously, cyclostationary properties of the periodic-like signals were utilized to determine the time-varying autocorrelation function (TVAC). In this work, multiple period determination was considered in aforementioned signals. First we proved that TVAC can be expressed in terms of Ramanujan sums, then we used TVAC in the periodicity metric to identify the periods. Periodicity metric provided energy distribution of the each periodic component as a function of block folding index and peaked at the points of the hidden periods in the signal. Proposed method was verified by the simulated signals and mean errors versus signal to noise ratio were illustrated. Finally, hidden periods in the noisy respiration signals were estimated by the proposed method successfully.