Performance analysis of joint-sparse recovery from multiple measurement vectors with prior information via convex optimization

We address the problem of compressed sensing with multiple measurement vectors associated with prior information in order to better reconstruct an original sparse signal. This problem is modeled via convex optimization with ℓ2,1 - ℓ2,1 minimization. We establish bounds on the number of measurements required for successful recovery. Our bounds and geometrical interpretations reveal that if the prior information can decrease the statistical dimension and make it lower than that under the case without prior information, ℓ2,1 - ℓ2,1 minimization improves the recovery performance dramatically. All our findings are further verified via simulations.