Multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method

TL;DR: In this article, a multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method is proposed to solve the problem of recovering an original multiband signal from multiple observations with unknown sparsity after continuous-limited module conversion through sampling by a modulated broadband converter under an Xampling framework.

Abstract: The invention discloses a multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method, which relates to the technical field of information and communication. The multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method is provided for solving the problem of recovering an original multiband signal from multiple observed value vectors with unknown sparsity after continuous-limited module conversion through sampling by a modulated broadband converter under an Xampling framework. The multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method comprises the steps of: conducting self-adaptive estimation on sparsity of a signal; updating the sparsity with a given step length factor through repeated iteration so that the sparsity gradually approaches the actual sparsity of the signal; correcting a support set through a backtracking thought and a minimum mean square criterion; stopping iteration until an residual error is less than a set threshold value; and finally reconstructing an original multiband signal through pseudo inverse operation by utilizing the obtained complete support set. The multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method can achieve the analog reconstruction of the multiband signal based on compressed sensing.