基于变步长分阶自适应匹配追踪算法的振动数据重构方法研究

Vibration data reconstruction method based on variable step size stepwise adaptive matching pursuit

在处理多维海量振动数据时,存在极大的存储和传输压力问题,针对这一问题,对压缩感知理论进行了研究,在选择最优测量矩阵的基础上,提出了一种变步长分阶自适应匹配追踪(VSSStAMP)算法。该算法将匹配追踪、变步长迭代及自适应思想相结合,通过双重阈值和可变步长控制信号的重构精度,弥补了传统的重构算法需提前得到信号的稀疏度,以及稀疏度自适应匹配追踪算法(SAMP)重构结果与步长关联较大的不足,从而实现了步长自适应的振动信号重构。研究结果表明:相对于传统的重构算法和SAMP算法,VSSStAMP算法在均方误差和运行时间等方面均有所改善,有效地提高了振动信号重构的精度和效率。

Aiming at the problem of huge storage and transmission pressure in processing multi-dimensional massive vibration data,the theory of compressed sensing was studied,and based on the optimal measurement matrix,a variable step size stepwise adaptive matching pursuit(VSSStAMP)was proposed.It was combined with matching pursuit,variable step size iteration,and sparsity adaptive,and signal reconstruction accuracy was controlled by double thresholds and variable step size.The shortage was covered that the traditional reconstruction algorithms need to obtain the signal sparsity in advance,and the sparsity adaptive algorithm(SAMP)reconstruction result was greatly affected by the step size,to realize the reconstruction of the vibration signal adaptively.The results indicate that compared with the traditional reconstruction algorithm and SAMP algorithm,the VSSStAMP algorithm improves in terms of mean square error and running time,and effectively improves the accuracy and efficiency of vibration signal reconstruction.