An efficient method for acquiring and processing signals based on compressed sensing
COMPUTER MODELLING & NEW TECHNOLOGIES 2014 18(4) 89-94
School of Mathematics and Computer Science, Shanxi Datong University, Datong 037009, Shanxi, China
Compressed sensing (CS) theory provides a novel sensing/sampling and processing paradigm that breaks through the limitation of Nyquist rate to some applications. However, it is usually happened to the instability and redundancy of the acquired CS measurements. In view of this, we propose an efficient method to achieve adaptive minimal measurements with fewer measurements and good reconstruction performance by adding the pre-processing block into CS data acquiring and processing paradigm. In the proposed method, we firstly obtain the measurements to perfectly reconstruct the signal, and then design the optimization method to obtain adaptive minimal measurements by eliminating the redundant measurements. Experimental results show that the proposed method can obtain fewer measurements to perfectly reconstruct the signal than that of classical CS and sequential compressed sensing frameworks.