Projects per year
Abstract
We use deterministic and probabilistic methods to analyse the performance of compressed sensing matrices constructed from Hadamard matrices and pairwise balanced designs, previously introduced by a subset of the authors. In this paper we obtain upper and lower bounds on the sparsity of signals for which our matrices guarantee recovery. These bounds are tight to within a multiplicative factor of at most 4√2. We provide new theoretical results and detailed simulations which indicate that the construction is competitive with Gaussian random matrices, and that recovery is tolerant to noise. A new recovery algorithm tailored to the construction is also given.
Original language | English |
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Pages (from-to) | 4850-4859 |
Number of pages | 10 |
Journal | IEEE Transactions on Information Theory |
Volume | 63 |
Issue number | 8 |
DOIs | |
Publication status | Published - Aug 2017 |
Keywords
- combinatorial designs
- Compressed sensing
- compressed sensing
- Electronic mail
- Error correction
- Error correction codes
- Matrices
- signal recovery
- Sparse matrices
- Standards
Projects
- 1 Finished
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A new approach to compressed sensing
Horsley, D. (Primary Chief Investigator (PCI)), Bryant, D. (Chief Investigator (CI)) & Colbourn, C. (Partner Investigator (PI))
Australian Research Council (ARC)
1/01/12 → 31/12/14
Project: Research