基于片段相似性的1维信号去噪方法

One-dimensional Signal Denoizing Method Based on Segment Similarity

  • 摘要: 为进一步提高现有算法的去噪效果,提出了1维信号的相似信号片段定义和基于信号片段的1维信号去噪方法.首先将基于现有方法得到的去噪数据划分其为若干信号片段,根据相似信号片段的定义,构造各信号片段的相似片段集合,并基于信号片段间的相似程度计算相似片段集合中元素的权值;进而基于各元素的加权平均得到信号片段的去噪结果.然后,融合所有信号片段的去噪结果,得到整体的去噪信号.最后,针对两个测试信号,分别与小波去噪、主成分分析去噪、稀疏表示去噪等方法进行了对比实验.仿真结果表明,利用所提的去噪方法能有效提高信噪比,降低均方误差,验证了该方法的有效性和可行性.

     

    Abstract: To improve the effectiveness of existing denoizing algorithms, we propose a definition of segment similarity in one-dimensional signals and a signal denoizing method.First, the denoized signal is obtained and divided into a certain number of signal segments by using an existing method into a certain number of signal segments.Then, in accordance with the defined segment similarity, we construct a set of similar segment collections for each signal segment and calculate the similar segment weights of the elements in the collection based on their similarity.Third, the signal segment of denoizing result is acquired based on the weighted average of all elements.All the denoizing results of the signal segments are combined to obtain the denoizing result of the entire signal.Finally, two experiments are conducted.Experiment results showed that compared with wavelets, principal component analysis, and sparse representation, the proposed method can improve the signal-to-noise ratio and reduce the mean square error effectively.The results verify the effectiveness and the feasibility of the proposed method.

     

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