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We address the recovery of sparse vectors in an overcomplete, linear and noisy multiple measurement framework, where the measurement matrix is known upto a permutation of its rows. We derive sparse Bayesian learning (SBL) based updates for joint recovery of the unknown sparse vector and the sensing order, represented using a permutation matrix. We model the sparse matrix using multiple uncorrelated and correlated vectors, and in particular, we use the first order AR model for the correlatedarXiv:1802.00559v1 fatcat:gohzs3ifx5g5xf4fmm5rlgmbb4