[PDF] Top 20 Knowledge-aided covariance matrix estimation and adaptive detection in compound-Gaussian noise
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Knowledge-aided covariance matrix estimation and adaptive detection in compound-Gaussian noise
... embedded in colored noise modeled in terms of a com- pound-Gaussian ...The covariance matrices of the primary and the secondary data share a common structure while having ... Voir le document complet
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Knowledge-aided Bayesian covariance matrix estimation in compound-Gaussian clutter
... Terms— Covariance matrix, estimation, radar. 1. INTRODUCTION AND PROBLEM STATEMENT An ubiquitous task of most radar systems is to detect the presence of a target, in a given range cell, ... Voir le document complet
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Adaptive detection in elliptically distributed noise and under-sampled scenario
... considered in the sequel, the MF amounts to (17) The matched Þlter assumes that both and the density gen- erator are known, which is unrealistic but can serve as a ...some adaptive detection ... Voir le document complet
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Knowledge-aided bayesian detection in heterogeneous environments
... Terms—Bayesian detection, heterogenous environments, knowledge-aided processing, maximum a posteriori ...space and/or time signature, in a cell under test (CUT), in the presence ... Voir le document complet
4
Covariance matrix estimation with heterogeneous samples
... cretes in some range cells, ...example, in the case of a for- ward-looking radar, it is known that the clutter is distributed along an ellipse in the angle-Doppler plane, and that this el- ... Voir le document complet
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Bounds for a mixture of low-rank compound-Gaussian and white Gaussian noises
... INTRODUCTION AND PROBLEM STATEMENT I N RADAR applications, the optimal processing scheme for detecting a target buried in disturbance (typically clutter and thermal noise) consists, under the ... Voir le document complet
11
Siegel distance-based covariance matrix selection for Space-Time Adaptive
... areas, and by minimizing the number of detection tests. It consists in computing a SCM with a few training samples in the neighborhood of the CUT (so that we limit clutter heterogeneity which ... Voir le document complet
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On the use of Empirical Likelihood for non-Gaussian clutter covariance matrix estimation
... the noise as the sum of the clutter (due to the ground echoes) and the thermal noise ...resulting noise can apart from a SIRV and previous results become ...suboptimal. In these ... Voir le document complet
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Estimation of Toeplitz Covariance Matrices in Large Dimensional Regime With Application to Source Detection
... is Hermitian nonnegative such that sup T kΓ T k < ∞. We have here a model for a rank-one signal corrupted with a Gaussian spatially white and temporally correlated noise with stationary temporal ... Voir le document complet
11
Robust covariance matrix estimation and portfolio allocation: the case of non-homogeneous assets
... Sample Covariance Matrix (SCM) is an optimal estimator of the ...tails and asymmetry hardly compatible with the Gaussian ...robust covariance estimation under non-Gaussian ... Voir le document complet
7
Generalized Likelihood Ratio Test for Detection of Gaussian Rank-One Signals in Gaussian Noise With Unknown Statistics
... attention, and the quasi totality of recent studies followed the lead of [2] and considered α t p as deter- ministic ...authors knowledge, no references have addressed detection of a ... Voir le document complet
12
Adaptive detection of a Gaussian signal in Gaussian noise
... Optronics and Signal 10 Avenue Edouard Belin, 31055 Toulouse France Abstract—Adaptive detection of a Swerling I-II type target in Gaussian noise with unknown covariance ... Voir le document complet
5
An adaptive detection of spread targets in locally Gaussian clutter using a long integration time
... target in ground clutter, using a long integration ...time adaptive processing (STAP) cannot be ...known in- terference subspace in the Doppler domain depending on its radial and ... Voir le document complet
5
Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: A Bayesian approach
... Adaptive detection of distributed targets has been addressed in [1] and [2]; noise is modeled in terms of independent, com- plex normal random vectors with a common ... Voir le document complet
12
Invariance properties of the likelihood ratio for covariance matrix estimation in some complex elliptically contoured distributions
... random matrix decompositions: distributions, ...spherical matrix distributions, in: ...shrinkage estimation for elliptically symmetric distributions with unknown covariance ... Voir le document complet
11
A Stein’s approach to covariance matrix estimation using regularization of Cholesky factor and log-Cholesky metric
... Selliah in Selliah ( 1964 ). In Tsukuma and Kubokawa ( 2016 ) extensions of these estimators to the case p < n are ...indeed, in order to whiten data, only a triangular system of equations ... Voir le document complet
10
Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions
... learning and signal processing applications require an adequate framework to compare statistical ob- jects, starting with probability ...[1] and later by Kan- torovich [2] in a transport theory ... Voir le document complet
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Bootstrapping heteroskedasticity consistent covariance matrix estimator
... Introduction In this paper, we show that the wild bootstrap covariance matrix estimator can be calculated directly, without simulation, since it is simply a traditional heteroskedasticity consistent ... Voir le document complet
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Adaptive Detection of Coherent Radar Targets in the Presence of Noise Jamming
... reside in the signal processing unit of the system without the need of additional ...the detection problem as a binary hypothesis test where primary data (namely those containing target returns) are formed ... Voir le document complet
14
Adaptive filtering for estimation of a low-rank positive semidefinite matrix
... parameter estimation problem from the model (11) is a linear ...nonlinear and nonconvex. Experimentally, both algorithms (19) and (23)-(24) are well-behaved, and their convergence properties ... Voir le document complet
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