Compressive Sensing Sparse Sampling Method for Composite Material Based on Principal Component Analysis

Signals can be sampled by compressive sensing theory with a much less rate than those by traditional Nyquist sampling theorem, and reconstructed with high probability, only when signals are sparse in the time domain or a transform domain. Most signals are not sparse in real world, but can be expressed in sparse form by some kind of sparse transformation. Commonly used sparse transformations will lose some information, because their transform bases are generally fixed. In this paper, we use principal component analysis for data reduction, and select new variable with low dimension and linearly correlated to the original variable, instead of the original variable with high dimension, thus the useful data of the original signals can be included in the sparse signals after dimensionality reduction with maximize portability. Therefore, the loss of data can be reduced as much as possible, and the efficiency of signal reconstruction can be improved. Finally, the composite material plate is used for the experimental verification. The experimental result shows that the sparse representation of signals based on principal component analysis can reduce signal distortion and improve signal reconstruction efficiency.