LDA-MIG detectors for maritime targets in nonhomogeneous sea clutter
This article deals with the problem of detecting maritime targets embedded in
nonhomogeneous sea clutter, where the limited number of secondary data is available due …
nonhomogeneous sea clutter, where the limited number of secondary data is available due …
Hyperspectral anomaly detectors using robust estimators
J Frontera-Pons, MA Veganzones… - IEEE Journal of …, 2015 - ieeexplore.ieee.org
Anomaly detection methods are devoted to target detection schemes in which no a priori
information about the spectra of the targets of interest is available. This paper reviews …
information about the spectra of the targets of interest is available. This paper reviews …
Regularized -Estimators of Scatter Matrix
E Ollila, DE Tyler - IEEE Transactions on Signal Processing, 2014 - ieeexplore.ieee.org
In this paper, a general class of regularized M-estimators of scatter matrix are proposed that
are suitable also for low or insufficient sample support (small n and large p) problems. The …
are suitable also for low or insufficient sample support (small n and large p) problems. The …
A signal processing perspective on financial engineering
Y Feng, DP Palomar - Foundations and Trends® in Signal …, 2016 - nowpublishers.com
Financial engineering and electrical engineering are seemingly different areas that share
strong underlying connections. Both areas rely on statistical analysis and modeling of …
strong underlying connections. Both areas rely on statistical analysis and modeling of …
Sparse and low-rank matrix decomposition for automatic target detection in hyperspectral imagery
AW Bitar, LF Cheong, JP Ovarlez - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Given a target prior information, our goal is to propose a method for automatically separating
targets of interests from the background in hyperspectral imagery. More precisely, we regard …
targets of interests from the background in hyperspectral imagery. More precisely, we regard …
Shrinking the eigenvalues of M-estimators of covariance matrix
A highly popular regularized (shrinkage) covariance matrix estimator is the shrinkage
sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but …
sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but …
Heterogeneous clutter suppression via affine transformation on Riemannian manifold of HPD matrices
X Chen, Y Cheng, H Wu, H Wang - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Due to a serious shortage of training data, the performance of adaptive clutter suppression
suffers remarkable degradation in heterogeneous environments. To address this problem, a …
suffers remarkable degradation in heterogeneous environments. To address this problem, a …
Regularized Tyler's scatter estimator: Existence, uniqueness, and algorithms
This paper considers the regularized Tyler's scatter estimator for elliptical distributions,
which has received considerable attention recently. Various types of shrinkage Tyler's …
which has received considerable attention recently. Various types of shrinkage Tyler's …
Robust adaptive detection of buried pipes using GPR
Q Hoarau, G Ginolhac, AM Atto, JM Nicolas - Signal Processing, 2017 - Elsevier
Detection of buried objects such as pipes using a Ground Penetrating Radar (GPR) is
intricate for three main reasons. First, noise is important in the resulting image because of …
intricate for three main reasons. First, noise is important in the resulting image because of …
A robust statistics approach to minimum variance portfolio optimization
L Yang, R Couillet, MR McKay - IEEE Transactions on Signal …, 2015 - ieeexplore.ieee.org
We study the design of portfolios under a minimum risk criterion. The performance of the
optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio …
optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio …