  Iterative reweighted algorithms, as a class of algorithms for sparse signal
recovery, have been found to have better performance than their non-reweighted
counterparts. However, for solving the problem of multiple measurement vectors
(MMVs), all the existing reweighted algorithms do not account for temporal
correlation among source vectors and thus their performance degrades
significantly in the presence of correlation. In this work we propose an
iterative reweighted sparse Bayesian learning (SBL) algorithm exploiting the
temporal correlation, and motivated by it, we propose a strategy to improve
existing reweighted $\ell_2$ algorithms for the MMV problem, i.e. replacing
their row norms with Mahalanobis distance measure. Simulations show that the
proposed reweighted SBL algorithm has superior performance, and the proposed
improvement strategy is effective for existing reweighted $\ell_2$ algorithms.
