The problem central to sparse recovery and compressive sensing is that of stable sparse recovery: we want a distribution A of matrices A ∈ Rm×n such that, for any x ∈ R and with probability 1−δ > 2/3 over A ∈ A, there is an algorithm to recover x̂ from Ax with ‖x̂− x‖p ≤ C min k-sparse x′ ∥∥x− x′∥∥ p (1) for some constant C > 1 and norm p. The measurement complexity of this problem is well unders...

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 vectors and the sensing order, represented using a permutation matrix. We model the sparse vectors using multiple uncorr...