Bayesian Robust Signal Subspace Estimation in Non-Gaussian Environment
Résumé
In this paper, we focus on the problem of low rank
signal subspace estimation. Specifically, we derive new subspace
estimator using the Bayesian minimum mean square distance
formulation. This approach is useful to overcome the issues
of low sample support and/or low signal to noise ratio. In
order to be robust to various signal distributions, the proposed
Bayesian estimator is derived for a model of sources plus outliers,
following both a compound Gaussian distribution. In addition,
the commonly assumed complex invariant Bingham distribution
is used as prior for the subspace basis. Finally, the interest of the
proposed approach is illustrated by numerical simulations and
with a real data set for a space time adaptive processing (STAP)
application.
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