Paper Info
Title
Constrained subspace estimation via convex optimization.
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.
Year
Venue
Keywords
2017
European Signal Processing Conference
Subspace averaging,Grassmann manifold,convex optimization,semidefinite relaxation
Field
DocType
ISSN
Beamforming,Mathematical optimization,Subspace topology,Computer simulation,Signal-to-noise ratio,Algorithm,Linear subspace,Grassmannian,Convex optimization,Mathematics,Manifold
Conference
2076-1465
Citations 
PageRank 
References 
0
0.34
12
Authors
6
Name
Order
Citations
PageRank
Ignacio Santamaría194181.56
Javier Vía241240.39
Michael Kirby313714.40
Tim Marrinan472.55
Chris Peterson5296.26
Louis L. Scharf62525414.45