用EMD和小波消噪的加速度信号压缩重构新方法

针对噪声破坏加速度信号稀疏性、降低其压缩感知重构算法性能问题,提出了一种用经验模态分解(EMD)和小波分析联合消噪的加速度信号压缩重构新方法.该方法首先采用EMD和小波阈值联合消噪方法对加速度信号消噪处理,保持加速度信号内在稀疏性;然后基于压缩感知理论和加速度信号块结构信息,采用块稀疏贝叶斯学习算法以高概率重构原始加速度信号.采用USC-HAD人体日常行为数据库中的加速度信号验证本文方法的有效性.实验结果表明,本文所提方法的信噪比和均方根误差明显优于未经消噪处理的压缩感知重构算法,能够有效抑制加速度信号噪声,增大加速度信号稀疏度,提高加速度信号压缩重构算法性能.

New Method for Compression and Reconstruction of Acceleration Signal Using EMD and Wavelet Denoising

In order to solve the issue of the deterioration of reconstruction performance in compressed sensing(CS) for acceleration signal with poor sparsity that is produced by noise, this paper proposed a new method for perfect reconstruction of acceleration signal by using empirical mode decomposition(EMD) and wavelet denoising. Its basic idea is that the best sparsity of acceleration signal is firstly gained by using EMD and wavelet threshold denoising method. And then, considering CS and acceleration signal with block structure, block sparse Bayesian learning algorithm is applied to perfectly reconstruction original acceleration signal. The acceleration signal from human activity dataset USC-HAD is selected to test the effectiveness of the proposed method. The experimental results show that when compared to traditional CS algorithm without pre-denoising processing, the proposed method can obtain the best value of signal-to-noise ratio and root mean square error. Also, these results suggested that our proposed method has the superior ability of denoise for gaining best sparsity of acceleration signal, which significantly improve the reconstruction performance of CS.