Abstract
Traditional sparse reconstruction algorithms require that the dictionary is accurately known, and the sparse recovery performance would degrade significantly with the measurement matrix uncertainty. In order to eliminate the effects of measurement matrix uncertainty, this paper proposed a robust reconstruction algorithm. Through the variance of additive errors, this algorithm incorporates the sparsity constraint and the effect of the additive errors from the statistical point at the same time. Numerical simulations demonstrated that the proposed method has a better reconstructing performance than the traditional methods.
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