  Simultaneous orthogonal matching pursuit (SOMP) and block OMP (BOMP) are two
widely used techniques for sparse support recovery in multiple measurement
vector (MMV) and block sparse (BS) models respectively. For optimal
performance, both SOMP and BOMP require \textit{a priori} knowledge of signal
sparsity or noise variance. However, sparsity and noise variance are
unavailable in most practical applications. This letter presents a novel
technique called generalized residual ratio thresholding (GRRT) for operating
SOMP and BOMP without the \textit{a priori} knowledge of signal sparsity and
noise variance and derive finite sample and finite signal to noise ratio (SNR)
guarantees for exact support recovery. Numerical simulations indicate that GRRT
performs similar to BOMP and SOMP with \textit{a priori} knowledge of signal
and noise statistics.
