  In this paper, a novel method based on the entropy estimation of the
observation space eigenvalues is proposed to estimate the number of the sources
in Gaussian and Non-Gaussian noise. In this method, the eigenvalues of
correlation matrix of the observation space will be divided by two sets:
eigenvalues of signal subspace and eigenvalues of noise subspace. We will use
estimated entropy of eigenvalues to determine the number of sources. In this
method we do not need any a priory information about signals and noise. The
advantages of the proposed algorithm based on the performance is compared with
the existing methods in the presence of Gaussian and Non-Gaussian noise. We
have shown that our proposed method outperforms those methods in the
literature, for different values of observation time and Signal to Noise Ratio,
i. e. SNR. It is shown that the algorithm is consistent and also its
probability of false alarm and probability of missed detection tend to zero for
long observation time.
