International audienceThis paper addresses structured scatter matrix estimation within the non convex set of Kronecker product structure. The latter model usually involves two matrices , which can be themselves linearly constrained, and arises in many applications, such as MIMO communication , MEG/EEG data analysis. Taking this prior knowledge into account generally improves estimation accuracy. In the framework of robust estimation, the t-distribution is particularly suited to model heavy-tailed data. In this context, we introduce an estimator of the scatter matrix, having a Kronecker product structure and potential linear structured factors. In addition, we show that the proposed method yields a consistent and efficient estimate
International audienceIn most modern signal processing applications, observations are generally mode...
International audienceIn most modern signal processing applications, observations are generally mode...
International audienceIn most modern signal processing applications, observations are generally mode...
International audienceThis paper addresses structured scatter matrix estimation within the non conve...
International audienceThis paper addresses structured scatter matrix estimation within the non conve...
International audienceThis paper addresses structured scatter matrix estimation within the non conve...
International audienceThe estimation of covariance matrices is a core problem in many modern adaptiv...
International audienceThe estimation of covariance matrices is a core problem in many modern adaptiv...
International audienceThe estimation of covariance matrices is a core problem in many modern adaptiv...
International audienceThe estimation of covariance matrices is a core problem in many modern adaptiv...
International audienceThis paper addresses structured covariance matrix estimation under t-distribut...
International audienceThis paper addresses structured covariance matrix estimation under t-distribut...
International audienceThis paper addresses structured covariance matrix estimation under t-distribut...
International audienceThis paper addresses structured covariance matrix estimation under t-distribut...
Abstract—A number of signal processing applications require the estimation of covariance matrices. S...
International audienceIn most modern signal processing applications, observations are generally mode...
International audienceIn most modern signal processing applications, observations are generally mode...
International audienceIn most modern signal processing applications, observations are generally mode...
International audienceThis paper addresses structured scatter matrix estimation within the non conve...
International audienceThis paper addresses structured scatter matrix estimation within the non conve...
International audienceThis paper addresses structured scatter matrix estimation within the non conve...
International audienceThe estimation of covariance matrices is a core problem in many modern adaptiv...
International audienceThe estimation of covariance matrices is a core problem in many modern adaptiv...
International audienceThe estimation of covariance matrices is a core problem in many modern adaptiv...
International audienceThe estimation of covariance matrices is a core problem in many modern adaptiv...
International audienceThis paper addresses structured covariance matrix estimation under t-distribut...
International audienceThis paper addresses structured covariance matrix estimation under t-distribut...
International audienceThis paper addresses structured covariance matrix estimation under t-distribut...
International audienceThis paper addresses structured covariance matrix estimation under t-distribut...
Abstract—A number of signal processing applications require the estimation of covariance matrices. S...
International audienceIn most modern signal processing applications, observations are generally mode...
International audienceIn most modern signal processing applications, observations are generally mode...
International audienceIn most modern signal processing applications, observations are generally mode...