TY - JOUR

T1 - Target Detection within Nonhomogeneous Clutter Via Total Bregman Divergence-Based Matrix Information Geometry Detectors

AU - Hua, Xiaoqiang

AU - Ono, Yusuke

AU - Peng, Linyu

AU - Cheng, Yongqiang

AU - Wang, Hongqiang

N1 - Publisher Copyright:
© 1991-2012 IEEE.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Matrix information geometry (MIG) detector

KW - Matrix manifold

KW - Nonhomogeneous clutter

KW - Total Bregman divergence (TBD)

UR - http://www.scopus.com/inward/record.url?scp=85112732946&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85112732946&partnerID=8YFLogxK

U2 - 10.1109/TSP.2021.3095725

DO - 10.1109/TSP.2021.3095725

M3 - Article

AN - SCOPUS:85112732946

SN - 1053-587X

VL - 69

SP - 4326

EP - 4340

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

M1 - 9479799

ER -