Compressed sensing signal reconstruction method based on conjugate gradient method

Abstract

The invention discloses a compressed sensing signal reconstruction method based on a conjugate gradient method. According to the invention, under the condition of known sensing matrixes and observed signals, the reconstruction of a source signal with sparsity under the white gaussian noise environment can be achieved by the conjugate gradient method. According to the compressed sensing signal reconstruction method, the deficiency that in the prior art the reconstruction method based on orthogonal matching pursuit is high in complexity and the deficiency that the prior art based on the gradient descent method is slow in rate of convergence and low in reconstruction accuracy are overcome, thus the source signal can be reconstructed with lower complexity and higher reconstruction accuracy in actual application.

Description

technical field [0001] The invention belongs to the technical field of communication, and further relates to a method for reconstructing a compressed sensing signal based on a conjugate gradient method in the technical field of signal processing. The invention can realize the reconstruction of the source signal with sparseness under the Gaussian white noise environment under the condition that the perception matrix and the observation signal are known. Background technique [0002] Compressed sensing (Compressed Sensing, CS) breaks through the traditional Nyquist sampling theorem, and can sample signals at a rate much lower than the Nyquist sampling rate. A reconstruction method with low complexity, fast convergence speed and high reconstruction accuracy is used to reconstruct the source signal. Therefore, it is of great significance to study the reconstruction method of compressed sensing signal. At present, the compression sensing signal reconstruction methods mainly incl...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that the amount of calculation required to update the source signal by orthogonal projection is large, so the complexity of the reconstruction method is high

However, the disadvantage of this method is that there is a "sawtooth effect" when using the gradient descent method to reconstruct the source signal, so the convergence speed of the reconstruction method is slow and the reconstruction accuracy is low

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