Compressed sensing is a novel theory for signal processing which breaks through the sampling limitation based on traditional Shannon sampling theory

and makes it into reality that one can efficiently acquire and exactly reconstruct a signal using the prior knowledge that it is sparse or compressible.In reality

however

some signals exhibit additional structures

the typical example is the signal which is called block-sparse signal

i.e.

the non-zero coefficients appear in a few fixed blocks.In order to tackle such block-sparse signal

in this paper we investigate the iterative reweighed least squares algorithm for block-sparse compressed sensing.The error estimation and local convergence analysis have been established.We simultaneously demonstrate the effectiveness of the iterative reweighed least squares algorithm (IRLS) for block-sparse compressed sensing by simulation results.