Information Distances in Stochastic Resolution Analysis

A stochastic approach to resolution is explored that uses information distances computed from the geometry of data models characterized by the Fisher information in cases with spatial-temporal measurements for multiple parameters. Stochastic resolution includes probability of resolution at signal-to-noise ratio (SNR) and separation of targets. The probability of resolution is assessed by exploiting different information distances in likelihood ratios. Taking SNR into account is especially relevant in compressive sensing (CS) due to its fewer measurements. Our stochastic resolution is also compared with actual resolution from sparse-signal processing that is nowadays a major part of any CS sensor. Results demonstrate the suitability of the proposed analysis due to its ability to include crucial impacts on the performance guarantees: array configuration or sensor design, SNR, separation and probability of resolution.