International audienceThe MUSIC method is widely used in the field of DoA estimation using an array of M sensors, and is known to perform well as long as the number of available samples N is much larger than M. Nevertheless , in the scenario where N is of the same order of magnitude than M , its performance degrades, essentially because the sample covariance matrix (SCM) is no more a good estimator. A classical improvement, known as "rectification", consists in forcing the SCM to have a Toeplitz structure. In this paper, we analyze this method, by considering the asymptotic regime where M, N both converge to infinity at the same rate, and by studying consistency and asymptotic normality of the related DoA estimates
In this article, the outlier production mechanism of the conventional Multiple Signal Classification...
MUltiple SIgnal Classification (MUSIC) is a standard technique for direction of arrival (DOA) estima...
In the direction of arrival (DOA) estimation problem, we encounter both finite data and insufficient...
International audienceThe MUSIC method is widely used in the field of DoA estimation using an array ...
International audienceThe MUSIC method is widely used in the field of DoA estimation using an array ...
International audience—When using subspace methods for DoA estimation such as MUSIC, it is well know...
International audience—This paper adresses the statistical performance of subspace DoA estimation us...
International audienceThis paper adresses the statistical performance of subspace DoA estimation usi...
This paper addresses the statistical performance of subspace DoA estimation using a sensor array, in...
This paper addresses the statistical behaviour of the MUSIC method for DoA estimation, in a scenario...
Subspace based direction-of-arrival (DOA) estimation has attracted many excellent performance studie...
The statistical behavior of MUSIC under the presence of DOA dependent random perturbations is invest...
This paper considers the statistical performance of the MUSIC method under the condition that two cl...
Two problems are considered in this dissertation: (1) Direction-of-arrival estimation analysis, and ...
In this paper, we introduce a new framework for robust multiple sig-nal classification (MUSIC). The ...
In this article, the outlier production mechanism of the conventional Multiple Signal Classification...
MUltiple SIgnal Classification (MUSIC) is a standard technique for direction of arrival (DOA) estima...
In the direction of arrival (DOA) estimation problem, we encounter both finite data and insufficient...
International audienceThe MUSIC method is widely used in the field of DoA estimation using an array ...
International audienceThe MUSIC method is widely used in the field of DoA estimation using an array ...
International audience—When using subspace methods for DoA estimation such as MUSIC, it is well know...
International audience—This paper adresses the statistical performance of subspace DoA estimation us...
International audienceThis paper adresses the statistical performance of subspace DoA estimation usi...
This paper addresses the statistical performance of subspace DoA estimation using a sensor array, in...
This paper addresses the statistical behaviour of the MUSIC method for DoA estimation, in a scenario...
Subspace based direction-of-arrival (DOA) estimation has attracted many excellent performance studie...
The statistical behavior of MUSIC under the presence of DOA dependent random perturbations is invest...
This paper considers the statistical performance of the MUSIC method under the condition that two cl...
Two problems are considered in this dissertation: (1) Direction-of-arrival estimation analysis, and ...
In this paper, we introduce a new framework for robust multiple sig-nal classification (MUSIC). The ...
In this article, the outlier production mechanism of the conventional Multiple Signal Classification...
MUltiple SIgnal Classification (MUSIC) is a standard technique for direction of arrival (DOA) estima...
In the direction of arrival (DOA) estimation problem, we encounter both finite data and insufficient...