Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 135-139.

• Intelligent Computing • Previous Articles     Next Articles

Low Complexity Bayesian Sparse Signal Algorithm Based on Stretched Factor Graph

BIAN Xiao-li   

  1. Zhengzhou Vocational College of Finance and Taxation,Zhengzhou 450048,China
  • Online:2018-06-20 Published:2018-08-03

Abstract: The linear mathematical model of additive Gauss white noise was established,and the message passing algorithm based on Sparse Bayesian learning was studied in this model.In this work,we modified the factor graph by adding some extra hard constraints which enables the use of combined belief propagation (BP) and MF message passing.This paper proposed a low complexity BP-MF SBL algorithm,based on which an approximate BP-MF SBL algorithm was also developed to further reduce the complexity.The BP-MF SBL algorithms show their merits compared with state-of-the-art MF SBL algorithms.They deliver even better performance with much lower complexity compared with the vector-form MF SBL algorithm and they significantly outperform the scalar-form MF SBL algorithm with similar complexity.

Key words: Additive Gauss white noise, BP-MF SBL algorithm, Low complexity, Sparse Bayesian learning, Stretched factor graph

CLC Number: 

  • TP301
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