利用改进的稀疏表示处理FBG传感信号

FBG sensing signal denoised with improved sparse representation method

基于压缩感知（CS）的正交匹配追踪（OMP）算法,须以稀疏度确定为先验条件,在实际应用中稀疏度不易确定的情况下,本文提出了稀疏度确定方法和二次正交匹配追踪（TOMP）算法。先引入熵权法采用多指标融合并结合饱和值点法确定稀疏度,然后利用所提方法对实验信号进行重构。实验仿真结果表明：与同类算法相比,本文所提TOMP算法增加0.1s运行时间降低了12-22%的重构误差,更好折中处理了重构误差和时间;与不同类算法相比,本文所提方法重构的信号信噪比（SNR）最大可提升22dB,且均方根误差（RMSE）降低0.7,因此去噪效果更优。

Orthogonal matching pursuit（OMP）algorithm of compressive sensing should take sparsity as apriori condition,but the actual sparsity is not easy to be determined.In this paper,the method to determine sparsity and twice orthogonal matching pursuit algorithm（TOMP）are proposed.Firstly,the entropy-weight method is introduced to determine the sparsity with combination multi-index fusion and the method of saturation point,and then the proposed method applied to reconstruct the signal.Simulation results show that,compared to the similar algorithm,the signal processed by the proposed algorithm increased 0.1second to reduce 12-22%reconstructon error;while compared with different algorithms,signal noise ratio（SNR）of the signal after processing by the proposed method can be improved by 22 dB,and root mean square error（RMSE）can be reduced by 0.7,so it demonstrates better de-noising effect.