Daubechies Wavelets Combined Sparse Basement Based on Compressed Sensing in Wireless Sensor Networks

A new method to construct a Daubechies wavelets combined sparse basement based on compressed sensing theory is proposed in this paper, which is particularly suitable for the sensing signals with time correlation characteristics. In this research, the simplified dictionary is constructed by Daubechies wavelets function after atoms checking and screening. This sparse basement is obtained by the coefficient vectors gotten from sparse decomposition of 24 elementary model signals. The simulation results show that this method has more advantages compared with existing algorithms, including low computational complexity, small storage requirement, short construction time and good sparse effect.