Abstract:
In block compressed sensing, the retrieval of block sparse signals from an under determinedsystem of linear equations is of main interest. The successful recovery of
such signals depends on the optimally designed sensing matrix with good coherence
properties. Therefore, several families of matrices having optimal coherence properties
with increased block size are required to be investigated. Such investigation will led to
an improved block sparse signal reconstruction. The two coherence metrics i.e., block
coherence and sub-coherence are important to analyze when considering the optimally
designed sensing matrix.
The implication of the outcome presented lies in the fact that smaller the coherence
parameters, better the recovery performance. Moreover, exploitation of block sparsity
with certain conditions resulted in successful recovery for a higher sparsity level than
treating the signal as conventionally sparse. The overall results confirmed that deterministicsensing matrices offer better results as compared to random sensing matrices.
