  In this paper, we consider multiple signals sharing same instantaneous
frequencies. This kind of data is very common in scientific and engineering
problems. To take advantage of this special structure, we modify our
data-driven time-frequency analysis by updating the instantaneous frequencies
simultaneously. Moreover, based on the simultaneously sparsity approximation
and fast Fourier transform, some efficient algorithms is developed. Since the
information of multiple signals is used, this method is very robust to the
perturbation of noise. And it is applicable to the general nonperiodic signals
even with missing samples or outliers. Several synthetic and real signals are
used to test this method. The performances of this method are very promising.
