Adaptive coherence analysis of nonstationary time series using the adaptive lomb spectrum

This paper proposes an adaptive spectral coherence analysis of nonstationary time series using the adaptive Lomb spectrum. Unlike conventional coherence analysis using the Fourier transform, the proposed algorithm employs Lomb spectrum as the basic spectral analysis tool and the intersection of confidence intervals (ICI) rule for computing adaptively the window sizes to achieve a better time-frequency resolution of the coherence and the associated phase differences. Simulation results show that the proposed method can achieve a better time-frequency resolution than other conventional coherence measurements for nonstationary signals