1 research outputs found
Performance Analysis of Joint-Sparse Recovery from Multiple Measurements and 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 matrix signal. minimization is used to
emphasize co-sparsity property and similarity between matrix signal and prior
information. We then derive the necessary and sufficient condition of
successfully reconstructing the original signal and establish the lower and
upper bounds of required measurements such that the condition holds from the
perspective of conic geometry. Our bounds further indicates what prior
information is helpful to improve the the performance of CS. Experimental
results validates the effectiveness of all our findings