基于拉伸因子图的低复杂度贝叶斯稀疏信号算法研究

Low Complexity Bayesian Sparse Signal Algorithm Based on Stretched Factor Graph

建立加性高斯白噪声的线性数学模型,针对此模型对基于稀疏贝叶斯学习的消息传递算法进行研究。对传统的因子图通过添加额外的硬约束节点得到改进的因子图,然后在改进的因子图中利用联合BP-MF规则,提出低复杂度的BP-MF SBL算法。为了进一步降低复杂度,在BP-MF SBL的基础上提出近似BP-MF SBL算法。仿真结果表明与向量形式的MF算法相比,所提方法复杂度低,且性能有所提升;与标量形式的MF算法相比,在复杂度相似的情况下,所提方法的性能更好。

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-ofthe-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.