Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

Compressive sensing allows the reconstruction of original signals from a much smaller number samples as compared to Nyquist sampling rate. The effectiveness compressive motivated researchers for its deployment in variety application areas. use an efficient matrix high-performance recovery algorithms improves performance framework significantly. This paper presents underlying concepts well previous work done targeted domains accordance with various To develop prospects within available functional blocks frameworks, diverse range areas are investigated. three fundamental elements (signal sparsity, subsampling, and reconstruction) thoroughly reviewed this by becoming acquainted key research gaps previously identified community. Similarly, basic mathematical formulation is used outline some primary evaluation metrics 1D 2D sensing.