  Transmitted data may be corrupted by both noise and data loss. Grassmannian
frames are in some sense optimal representations of data transmitted over a
noisy channel that may lose some of the transmitted coefficients. Fusion frame
(or frame of subspaces) theory is a new area that has potential to be applied
to problems in such fields as distributed sensing and parallel processing.
Grassmannian fusion frames combine elements from both theories. A simple, novel
construction of Grassmannian fusion frames with an extension to Grassmannian
fusion frames with local frames shall be presented. Some connections to sparse
representations shall also be discussed.
