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This letter studies the sensing performance of random Bernoulli matrices with column size much larger than row size . It is observed that as the compression ratio increases, this kind of matrices tends to present a performance floor regarding the guaranteed signal sparsity. The performance floor is effectively estimated with the formula . To the best of our...
For the random {0,±1} ternary matrix, it is interesting to determine the number of nonzero elements required for good compressed sensing performance. By seeking the best RIP, this paper proposes a semi-deterministic ternary matrix, which is of deterministic nonzero positions but random signs. In practice, it presents better performance than common random ternary matrices and Gaussian random matrices.
We present the sparse binary matrix defined by low-density parity-check (LDPC) codes as measurement matrix in compressed sensing. This kind of matrix owns much stronger orthogonality than current other main measurement matrices. In practice, for a matrix given size, the optimal column degree for high orthogonality can also be determined by progressive edge-growth (PEG) algorithm.
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