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Compressive Sensing (CS) framework becomes well known since its ability to recover signal only by using less sampling required by Shanon-Nyquist theorem. The lack of required sampling is no longer constraint for having good reconstruction performance. The load is shifted to the reconstruction procedure instead of the sampling acquisition process. As long as the signal can be guaranteed sparse, the CS based method is able to provide high reconstruction accuracy. One of the CS principle is incoherence property, which can be represented by mutual coherence value. It represents the coherence between the sensing matrix and the sparse base dictionary. The theory said the less coherence between those two parameters, the more precise the reconstruction is. In fact, it is not consistently applied. The research presented on this paper find that, the theory is consistent for reconstruction on compression system, while it is not applied on the reconstruction of measurement system. Other properties are found to be more representative on assigning necessary condition for reconstruction performance on measurement system.

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