0000000000296926
AUTHOR
Samuli Visuri
Nonparametric statistics for DOA estimation in the presence of multipath
This paper is concerned with array signal processing in nonGaussian noise and in the presence of multipath. Robust and fully nonparametric high resolution algorithms for direction of arrival (DOA) estimation are presented. The algorithms are based on multivariate spatial sign and rank concepts. Spatial smoothing of the multivariate rank and sign based covariance matrices is employed as a preprocessing step in order to deal with coherent sources. The performance of the algorithms is studied using simulations. The results show that almost optimal performance is obtained in wide variety of different noise conditions.
Robust subspace DOA estimation for wireless communications
This paper is concerned with array signal processing in non-Gaussian noise typical in urban and indoor radio channels. Robust and fully nonparametric high resolution algorithms for direction of arrival (DOA) estimation are presented. The algorithms are based on multivariate spatial sign and rank concepts. The performance of the algorithms is studied using simulations. The results show that almost optimal performance is obtained in wide variety of noise conditions.
Affine equivariant multivariate rank methods
The classical multivariate statistical methods (MANOVA, principal component analysis, multivariate multiple regression, canonical correlation, factor analysis, etc.) assume that the data come from a multivariate normal distribution and the derivations are based on the sample covariance matrix. The conventional sample covariance matrix and consequently the standard multivariate techniques based on it are, however, highly sensitive to outlying observations. In the paper a new, more robust and highly efficient, approach based on an affine equivariant rank covariance matrix is proposed and outlined. Affine equivariant multivariate rank concept is based on the multivariate Oja (Statist. Probab. …
Sign and rank covariance matrices
The robust estimation of multivariate location and shape is one of the most challenging problems in statistics and crucial in many application areas. The objective is to find highly efficient, robust, computable and affine equivariant location and covariance matrix estimates. In this paper, three different concepts of multivariate sign and rank are considered and their ability to carry information about the geometry of the underlying distribution (or data cloud) are discussed. New techniques for robust covariance matrix estimation based on different sign and rank concepts are proposed and algorithms for computing them outlined. In addition, new tools for evaluating the qualitative and quant…