  Information divergences are commonly used to measure the dissimilarity of two
elements on a statistical manifold. Differentiable manifolds endowed with
different divergences may possess different geometric properties, which can
result in totally different performances in many practical applications. In
this paper, we propose a total Bregman divergence-based matrix information
geometry (TBD-MIG) detector and apply it to detect targets emerged into
nonhomogeneous clutter. In particular, each sample data is assumed to be
modeled as a Hermitian positive-definite (HPD) matrix and the clutter
covariance matrix is estimated by the TBD mean of a set of secondary HPD
matrices. We then reformulate the problem of signal detection as discriminating
two points on the HPD matrix manifold. Three TBD-MIG detectors, referred to as
the total square loss, the total log-determinant and the total von Neumann MIG
detectors, are proposed, and they can achieve great performances due to their
power of discrimination and robustness to interferences. Simulations show the
advantage of the proposed TBD-MIG detectors in comparison with the geometric
detector using an affine invariant Riemannian metric as well as the adaptive
matched filter in nonhomogeneous clutter.
