@InProceedings{8081398,
  author = {I. Santamaria and J. Via and M. Kirby and T. Marrinan and C. Peterson and L. Scharf},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Constrained subspace estimation via convex optimization},
  year = {2017},
  pages = {1200-1204},
  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.},
  keywords = {array signal processing;convex programming;optimisation;constrained subspace estimation problem;nonconvex problem;convex optimization;model based subspace;beamforming application;direction-of-arrival estimation;approximation based on a semidefinite relaxation;Estimation;Array signal processing;Signal to noise ratio;Manifolds;Europe;Signal processing algorithms;Subspace averaging;Grassmann manifold;convex optimization;semidefinite relaxation},
  doi = {10.23919/EUSIPCO.2017.8081398},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570346779.pdf},
}

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