基于聚分类图信号的稀疏恢复算法

A Sparse Restoration Algorithm Based on Clustered Class Graph Signals

图信号处理是解决不规则的数据最有效的方法之一,为此研究了基于聚分类图信号的稀疏恢复算法,针对复杂不规则的阵列信号,对相似性大的信号原子进行聚类分块,构造图信号空间结构,使用图滤波器设计相应的基于图信号的聚分类块匹配追踪算法。为了验证所提算法的有效性,与5种算法进行了对比实验,仿真实验表明:在相同采样率下,所提算法运行时间远小于其他主流算法,同时,在较小采样率下,所提算法具有较高的峰值信噪比。

Graph signal processing is one of the most effective methods to solve irregular data. For this reason, a sparse recovery algorithm based on clustered class graph signals is studied. For complex and irregular array signals,the similar signal atoms are clustered and divided into blocks, the spatial structure of the graph signal is constructed,and the corresponding clustered blocks orthogonal matching pursuit based on graph signal algorithm is designed by using graph filter. In order to verify the effectiveness of the proposed algorithm, a comparative experiment with five algorithms is carried out. Simulation experiments show that the running time of the proposed algorithm is much shorter than other mainstream algorithms under the same sampling rate, and at the same time, the proposed algorithm has a higher peak signal-to-noise ratio at a smaller sampling rate.