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Arnaud Breloy
Researcher at Paris West University Nanterre La Défense
Publications - 62
Citations - 282
Arnaud Breloy is an academic researcher from Paris West University Nanterre La Défense. The author has contributed to research in topics: Estimator & Covariance matrix. The author has an hindex of 7, co-authored 51 publications receiving 202 citations. Previous affiliations of Arnaud Breloy include University of Paris & University Institute of Technology, Burdwan University.
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Low-Complexity Algorithms for Low Rank Clutter Parameters Estimation in Radar Systems
TL;DR: Two algorithms based on the block majorization-minimization framework are derived and shown to be computationally faster than the state of the art, with guaranteed convergence in the context of a disturbance composed of a low rank heterogeneous and white Gaussian noise.
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Robust Covariance Matrix Estimation in Heterogeneous Low Rank Context
TL;DR: This paper derives an algorithm to compute the maximum likelihood estimator of the CM for the considered disturbance model and demonstrates performance on numerical simulations and on a space time adaptive processing for airborne radar application.
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Clutter Subspace Estimation in Low Rank Heterogeneous Noise Context
TL;DR: The fixed point equation that MLE of the CSP satisfies for a disturbance composed of a LR-SIRV clutter plus a zero mean WGN is introduced and a recursive algorithm is proposed to compute this solution.
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Intrinsic Cramér–Rao Bounds for Scatter and Shape Matrices Estimation in CES Distributions
TL;DR: In this paper, the Fisher Information Metric and its associated Riemannian distance (namely, CES-Fisher) on the manifold of Hermitian positive definite matrices are derived.
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Robust Estimation of Structured Scatter Matrices in (Mis)matched Models
TL;DR: This paper proposes a novel estimator, named StructurEd ScAtter Matrix Estimator (SESAME), which is based on a two-step estimation procedure based on the unbiasedness and the asymptotic efficiency and Gaussianity of SESAME, and derives a recur-sive estimation procedure that iteratively applies the S ESAME method.