  Correlation coefficient is usually used to measure the correlation degree
between two time signals. However, its performance will drop or even fail if
the signals are noised. Based on the time-frequency phase spectrum (TFPS)
provided by normal time-frequency transform (NTFT), similarity coefficient is
proposed to measure the similarity between two non-narrow-band time signals,
even if the signals are noised. The basic idea of the similarity coefficient is
to translate the interest part of signal f1(t)'s TFPS along the time axis to
couple with signal f2(t)'s TFPS. Such coupling would generate a maximum if
f1(t)and f2(t) are really similar to each other in time-frequency structure.
The maximum, if normalized, is called similarity coefficient. The location of
the maximum indicates the time delay between f1(t) and f2(t). Numerical results
show that the similarity coefficient is better than the correlation coefficient
in measuring the correlation degree between two noised signals. Precision and
accuracy of the time delay estimation (TDE) based on the similarity analysis
are much better than those based on cross-correlation (CC) method and
generalized CC (GCC) method under low SNR.
