@conference{10902/13017,
year = {2017},
url = {http://hdl.handle.net/10902/13017},
abstract = {Given a collection of M experimentally measured subspaces, and a model-based subspace, this paper addresses the problem of finding a subspace that approximates the collection, under the constraint that it intersects the model-based subspace in a predetermined number of dimensions. This constrained subspace estimation (CSE) problem arises in applications such as beamforming, where the model-based subspace encodes prior information about the direction-of-arrival of some sources impinging on the array. In this paper, we formulate the constrained subspace estimation (CSE) problem, and present an approximation based on a semidefinite relaxation (SDR) of this non-convex problem. The performance of the proposed CSE algorithm is demonstrated via numerical simulation, and its application to beamforming is also discussed.},
organization = {This work has been supported by the Ministerio de Economía, Industria y Competitividad (MINECO) of Spain, and AEI/FEDER funds of the E.U., under grants TEC2013-47141-C4-R (RACHEL), TEC2016-75067-C4-4-R (CARMEN) and TEC2016-81900-REDT (KERMES), and by the National Science Foundation under Grant No. IIS-1633830.},
publisher = {IEEE},
publisher = {25th European Signal Processing Conference (EUSIPCO), Kos island, Greece, 2017, 1200-1204},
title = {Constrained subspace estimation via convex optimization},
author = {Santamaría Caballero, Luis Ignacio and Vía Rodríguez, Javier and Kirby, Michael and Marrinan, Tim and Peterson, Chris and Scharf, Louis L.},
}