Abstract
Deterministic construction of measurement matrices for compressive sensing can be effected by first constructing a relatively small matrix explicitly, and then inflating it using a column replacement technique to form a large measurement matrix that supports at least the same level of sparsity. In particular, using easily developed null space conditions for ℓ0- and ℓ1-recoverability, properties of the pattern matrix used to select columns lead to well-studied matrices, separating and distributing hash families. Two-stage compression and recovery techniques are developed that employ more computationally intensive ℓ0-recoverability for small matrices and simpler ℓ1-recoverability for one larger matrix; this can reduce the number of measurements required.
Original language | English (US) |
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Article number | 5773644 |
Pages (from-to) | 1840-1845 |
Number of pages | 6 |
Journal | IEEE Transactions on Communications |
Volume | 59 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2011 |
Keywords
- Data compression
- combinatorial mathematics
- signal processing
ASJC Scopus subject areas
- Electrical and Electronic Engineering