  This paper examines the ability of greedy algorithms to estimate a block
sparse parameter vector from noisy measurements. In particular, block sparse
versions of the orthogonal matching pursuit and thresholding algorithms are
analyzed under both adversarial and Gaussian noise models. In the adversarial
setting, it is shown that estimation accuracy comes within a constant factor of
the noise power. Under Gaussian noise, the Cramer-Rao bound is derived, and it
is shown that the greedy techniques come close to this bound at high SNR. The
guarantees are numerically compared with the actual performance of block and
non-block algorithms, highlighting the advantages of block sparse techniques.
