Abstract
In order to operate properly, the superresolution
methods based on orthogonal subspace decomposition, such as
multiple signal classification (MUSIC) or estimation of signal
parameters by rotational invariance techniques (ESPRIT), need
accurate estimation of the signal subspace dimension, that is, of
the number of harmonic components that are superimposed and
corrupted by noise. This estimation is particularly difficult
when the S/N ratio is low and the statistical
properties of the noise are unknown. Moreover, in some
applications such as radar imagery, it is very important to avoid
underestimation of the number of harmonic components which are
associated to the target scattering centers. In this paper, we
propose an effective method for the estimation of the signal
subspace dimension which is able to operate against colored noise
with performances superior to those exhibited by the classical
information theoretic criteria of Akaike and Rissanen. The
capabilities of the new method are demonstrated through computer
simulations and it is proved that compared to three other methods
it carries out the best trade-off from four points of view, S/N
ratio in white noise, frequency band of colored noise, dynamic
range of the harmonic component amplitudes, and computing time.