一种新的压缩采样匹配追踪算法

New compressive sampling matching pursuit algorithm

提出了实用性更强的完全受噪声扰动理论模型,引入了与原信号相关的乘性噪声;并基于新的模型,提出了一种改进的压缩采样匹配追踪算法。该算法通过构造一个感知测量矩阵,在信号替代阶段中取代随机测量矩阵来减少相关性对支撑集筛选的影响,最后可在乘性噪声存在的情况下实现了信号的精确重建。实验结果表明,在相同测试条件下,该算法的重建效果均优于其他贪婪算法和基匹配法(basic pursuit,BP)。

This paper proposed a new more useful completely perturbed model, which incorporated muhiplicative noise corre- lated with the signal. It presented a new compressive sampling matching pursuit algorithm based on the new model. The pro- posed algorithm could recover the signal in high probability by constructing sensing measurement matrix which mitigated the co- herent interference to get the best support of the signal even in the present of multiplieative noise. The experimental results show that uaader the same condition, the proposed algorithm can get better reconstruction performances and it is superior to oth- er graedv Mgorithms and the BP algorithm.