A stochastic approach to resolution based on information distances computed from the geometry of data models which is characterized by the Fisher information is explored. Stochastic resolution includes probability of resolution and signal-to-noise ratio (SNR). The probability of resolution is assessed from a hypothesis test by exploiting information distances in a likelihood ratio. Taking SNR into account is especially relevant in compressive sensing (CS) due to its fewer measurements. Based on

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... this information-geometry approach, we demonstrate the stochastic resolution analysis in test cases from array processing. In addition, we also compare our stochastic resolution bounds with the actual resolution obtained numerically from sparse signal processing which nowadays is a major component of the back end of any CS sensor. Results demonstrate the suitability of the proposed stochastic resolution analysis due to its ability to include crucial features in the resolution performance guarantees: array configuration or sensor design, SNR, separation and probability of resolution.