利用概率结构稀疏模型实现信号重构的新算法

doi:10.3969/j.issn.1001-2400.2013.02.031

Abstract

Abstract:

In order to describe structured sparsity of the signal accurately, a probabilistic structured sparse model is constructed for signal reconstruction in compressive sensing(CS). Based on the structured sparse model, Boltzmann distribution is introduced to describe structured sparsity of the signal support rather than to describe the signal directly. Based on Bayesian CS, the maximum a posterior estimate of signal support is computed with the prior distribution and the Gaussian likelihood model of measurement, and then the signal is reconstructed using signal support. Experimental results show that, for the signal with the support known, the proposed algorithm is obviously superior to BP and OMP and that for the signal with the support unknown, its performance outperforms that of BP and OMP in the condition of a high measurement noise level and low reconstruction error tolerance.

Key words: compressive sensing, structured sparse model, signal support, Boltzmann distribution

HE Yibao;BI Duyan. Signal reconstruction algorithm based on the probabilistic structured sparse model[J].J4, 2013, 40(2): 194-200.

References

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Signal reconstruction algorithm based on the probabilistic structured sparse model

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