In this paper, we consider multiple signals sharing same instantaneous frequencies.
This kind of data is very common in scientic and engineering problems. To take ad-
vantage of this special structure, we modify our data-driven time-frequency analysis by
updating the instantaneous frequencies simultaneously. Moreover, based on the simul-
taneously sparsity approximation and fast Fourier transform, some ecient algorithms
is developed. Since the information of multiple signals is used, this method is very ro-
bust 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.