Top PDF Knowledge-aided Bayesian covariance matrix estimation in compound-Gaussian clutter
Knowledge-aided Bayesian covariance matrix estimation in compound-Gaussian clutter
... DGA. clutter returns are modeled as z k = √ τ k g k where g k is a Gaus- sian vector with covariance matrix R and τ k is a positive random variable, independent of g k ...proposed in the ...
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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 different power ...A ...
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Knowledge-aided STAP in heterogeneous clutter using a hierarchical bayesian algorithm
... the covariance matrix of a primary vector from heterogeneous samples and some prior knowledge is addressed, under the framework of knowledge-aided space-time adaptive processing ...a ...
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Knowledge-aided bayesian detection in heterogeneous environments
... 1. in [14], [15] in a rather different framework. Indeed, in [14] and [15], a beamformer is designed under the quadratic constraint that the output power corresponding to the a priori clutter ...
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Bounds for a mixture of low-rank compound-Gaussian and white Gaussian noises
... data matrix versus CNR. ν = 0.2. IV. CONCLUSION AND DISCUSSION In this paper, we investigated lower bounds for estimating the parameters of a mixture of a low-rank compound-Gaussian process ...
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Covariance matrix estimation with heterogeneous samples
... encountered in different problems, see, ...for covariance matrix estimation with compound-Gaussian clutter, and ends up with a similar implicit ...mentioned in ...
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Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: A Bayesian approach
... ONCLUSION In this paper, we have dealt with adaptive detection of dis- tributed targets embedded in colored noise modeled in terms of a compound-Gaussian process and without assuming ...
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Estimation des paramètres du clutter radar distribué selon la loi K
... le clutter est généralement de puissances ...du clutter et pour décrire les variations statistiques du clutter de mer, il est nécessaire d’utiliser un modèle non Gaussien tel que lognormal, Weibull, ...
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Video Covariance Matrix Logarithm for Human Action Recognition in Videos
... the covariance matrices is a manifold, that is not a vector space with the usual additive ...two covariance matrices, we need to apply a Riemannian met- ric in order to use the covariance ...
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Conditional expected likelihood technique for compound Gaussian and Gaussian distributed noise mixtures
... e.g., in HF direction finding applica- tions, external noise is dominated by lightning strikes and being practically white for linear uniform arrays is strongly non- ...Also, in radar applications, the main ...
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Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions
... lies in the comparative advantage of exploiting a particular covariance matrix distance in spe- cific ...obvious in our proofs and also deserves more ...
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Estimation des paramètres du clutter radar distribué selon la loi de Pareto
... le clutter est habituellement modélisé par une distribution de Rayleigh (L’intensité du clutter étant le carré de l’amplitude, elle est donc modélisée par une distribution Exponentielle) ...du ...
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Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments
... for estimation, it subsequently improves the estimation of the smoothness ...shown in [ZZ06] that the optimal samplings, for maximizing the log of the determinant of the Fisher information ...
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Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes
... HAL Id: hal-00906934 https://hal.archives-ouvertes.fr/hal-00906934 Preprint submitted on 20 Nov 2013 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, ...
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An analysis of covariance parameters in Gaussian Process-based optimization
... occurs in many real-world ...used in EGO is a Gaussian process (GP) conditional on data ...GP covariance function (or kernel), which is taken as a parameterized ...function. In this ...
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Bayesian nonparametric estimation of the spectral density of a long memory Gaussian time series
... with Gaussian weakly dependent data and, in a more general setting, Ghosal and Van der Vaart ...(2006). In this paper we study the asymptotic properties of the posterior distributions for ...
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Compressive Gaussian Mixture Estimation
... technique in machine ...cost in terms of memory and complexity when the vectors become numerous and/or high- ...vectors in a lower-dimensional ...moments in one pass over the ...tool in ...
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An adaptive detection of spread targets in locally Gaussian clutter using a long integration time
... target in ground clutter, using a long integration ...ground clutter can be modeled as a known in- terference subspace in the Doppler domain depending on its radial and orthoradial ...
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Estimation nonparamétrique de la structure de covariance des processus stochastiques
... L’estimation de la fonction de covariance est fortement liée à l’estimation de la densité spectrale du processus. C’est pourquoi au Chapitre 2 nous étudions le problème de l’es- timation nonparamétrique de la ...
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Aided Inertial Estimation of Wing Shape
... In computer vision, outdoor lighting poses additional problems. In fact, Kurita et al. [7] devised the flight plan for experiments to optimize lighting conditions to study static load deformations on a ...
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