Subspace averaging for source enumeration in large arrays

Ver/Abrir
Identificadores
URI: http://hdl.handle.net/10902/15194
ISBN: 978-1-5386-1572-0
ISBN: 978-1-5386-1571-3
Compartir
Estadísticas
Ver Estadísticas
Google Scholar
Registro completo
Mostrar el registro completo DC
Autoría
Santamaría Caballero, Luis IgnacioAutoridad Unican; Ramírez García, David; Scharf, Louis L.Autoridad Unican
Fecha
2018
Derechos
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publicado en
IEEE Statistical Signal Processing Workshop (SSP), Freiburg, Germany, 2018, 323-327
Editorial
IEEE
Enlace a la publicación
Palabras clave
Array processing
Grassmann manifold
Model order estimation
Source enumeration
Subspace averaging
Resumen/Abstract
Subspace averaging is proposed and examined as a method of enumerating sources in large linear arrays, under conditions of low sample support. The key idea is to exploit shift invariance as a way of extracting many subspaces, which may then be approximated by a single extrinsic average. An automatic order determination rule for this extrinsic average is then the rule for determining the number of sources. Experimental results are presented for cases where the number of array snapshots is roughly half the number of array elements, and sources are well separated with respect to the Rayleigh limit.
Colecciones a las que pertenece