A Gradient-based Algorithm for Optimizing Sensing Matrix with Normalization Constraint

作者：Lu Zeru; Bai Huang^*; Sun Binbin

来源：IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), 2016-06-05 To 2016-06-07.

DOI：10.1109/ICIEA.2016.7603990

• Coherence factors
• Compressive sensing
• Gradient based algorithm
• Matrix design
• Signal recovery

This paper deals with the problem of designing the sensing matrix F for a compressed sensing (CS) system, in which the dictionary. is assumed to be given. The optimal sensing matrix design is formulated as to identify those F which minimize a proposed coherence-based measure with the constraint that the equivalent dictionary A = Phi Psi is normalized. Unlike the existing measures, the proposed measure is defined as the sum of l(p)-norm-based coherence factors. A gradient-based algorithm is derived for solving this problem. Experiments are carried out and simulations show that the sensing matrix obtained by the proposed algorithm significantly improves the signal recovery accuracy of the CS system and outperforms those by existing algorithms.

• 出版日期2016
• 单位浙江工业大学