Subspace averaging and order determination for source enumeration

Abstract: In this paper, we address the problem of subspace averaging, with special emphasis placed on the question of estimating the dimension of the average. The results suggest that the enumeration of sources in a multi-sensor array, which is a problem of estimating the dimension of the array manifold, and as a consequence the number of radiating sources, may be cast as a problem of averaging subspaces. This point of view stands in contrast to conventional approaches, which cast the problem as one of identifiying covariance models in a factor model. We present a robust formulation of the proposed order fitting rule based on majorization-minimization algorithms. A key element of the proposed method is to construct a bootstrap procedure, based on a newly proposed discrete distribution on the manifold of projection matrices, for stochastically generating subspaces from a function of experimentally determined eigenvalues. In this way, the proposed subspace averaging (SA) technique determines the order based on the eigenvalues of an average projection matrix, rather than on the likelihood of a covariance model, penalized by functions of the model order. By means of simulation examples, we show that the proposed SA criterion is especially effective in high-dimensional scenarios with low sample support.

 Autoría: Garg V., Santamaria I., Ramirez D., Scharf L.,

 Fuente: IEEE Transactions on Signal Processing, 2019, 67(11), 3028-3041

Editorial: Institute of Electrical and Electronics Engineers Inc.

 Fecha de publicación: 01/06/2019

Nº de páginas: 14

Tipo de publicación: Artículo de Revista

DOI: 10.1109/TSP.2019.2912151

ISSN: 1053-587X,1941-0476

Proyecto español: TEC2016-75067-C4-4-R ; TEC2015-69648-REDC ; TEC2017-92552-EXP ; TEC2015-69868- C2-1-R ; TEC2017-86921-C2-2-R

Url de la publicación: https://doi.org/10.1109/TSP.2019.2912151