International audienceThis paper deals with radar detection in impulsive noise. Its aim is twofold. Firstly, assuming a Spherically Invariant Random Vectors (SIRV) modelling for the noise, the corresponding unknown covariance matrix is estimated by a recently introduced algorithm [1, 2]. A statistical analysis (bias, consistency, asymptotic distribution) of this estimate will be summarized allowing us to give the GLRT properties: the SIRV-CFAR (Constant False Alarm Rate) property, i.e. texture-CFAR and Covariance Matrix-CFAR, and the relationship between the Probability of False Alarm (PFA) and the detection threshold. Secondly, one of the main contributions of this paper is to give some results obtained with real non-Gaussian data. These r...
Abstract: The clutter encountered in low grazing an-gle situations is generally a non gaussian impul...
International audienceIn this paper, we study the adaptive version of the asymptotical Bayesian Opti...
In this paper, we study the adaptive version of the asymptot-ical Bayesian Optimum Radar Detector (B...
International audienceThis paper deals with radar detection in impulsive noise. Its aim is twofold. ...
This paper deals with radar detection in impulsive clutter. Its aim is twofold. Firstly, assuming a ...
This paper deals with radar detection in impulsive clutter. Its aim is twofold. Firstly, assuming a ...
International audienceIn this paper, we use the theory of generalized likelihood ratio tests (GLRT) ...
This thesis deals with radar detection in impulsive noise contexts. Indeed, under Gaussian assumptio...
In this paper, we use the theory of generalized likelihood ra-tio tests (GLRT) to study the adaptive...
The subject of detection of spatially distributed targets in non-Gaussian noise with unknown statist...
This paper presents a detailed theoretical analysis of a recently introduced covariance matrix estim...
New results are presented for coherent detection of radar signals with random parameters in correlat...
Abstract—This paper deals with covariance matrix estimation for radar detection in non-Gaussian nois...
International audienceThis paper deals with covariance matrix estimates in impulsive noise environme...
International audienceThis paper deals with covariance matrix estimation for radar detection in non-...
Abstract: The clutter encountered in low grazing an-gle situations is generally a non gaussian impul...
International audienceIn this paper, we study the adaptive version of the asymptotical Bayesian Opti...
In this paper, we study the adaptive version of the asymptot-ical Bayesian Optimum Radar Detector (B...
International audienceThis paper deals with radar detection in impulsive noise. Its aim is twofold. ...
This paper deals with radar detection in impulsive clutter. Its aim is twofold. Firstly, assuming a ...
This paper deals with radar detection in impulsive clutter. Its aim is twofold. Firstly, assuming a ...
International audienceIn this paper, we use the theory of generalized likelihood ratio tests (GLRT) ...
This thesis deals with radar detection in impulsive noise contexts. Indeed, under Gaussian assumptio...
In this paper, we use the theory of generalized likelihood ra-tio tests (GLRT) to study the adaptive...
The subject of detection of spatially distributed targets in non-Gaussian noise with unknown statist...
This paper presents a detailed theoretical analysis of a recently introduced covariance matrix estim...
New results are presented for coherent detection of radar signals with random parameters in correlat...
Abstract—This paper deals with covariance matrix estimation for radar detection in non-Gaussian nois...
International audienceThis paper deals with covariance matrix estimates in impulsive noise environme...
International audienceThis paper deals with covariance matrix estimation for radar detection in non-...
Abstract: The clutter encountered in low grazing an-gle situations is generally a non gaussian impul...
International audienceIn this paper, we study the adaptive version of the asymptotical Bayesian Opti...
In this paper, we study the adaptive version of the asymptot-ical Bayesian Optimum Radar Detector (B...