SFGPR Compressive Sensing Imaging Method Based on Deep Unfolding Network
Aiming at the problems of sensitive parameter selection and low imaging accuracy in the traditional compressive sensing imaging method of stepped frequency ground penetrating radar(SFGPR),a SFGPR compressive sensing imaging method based on deep unfolding network is proposed.This method first maps the iterative process of the fast iterative shrinkage threshold algorithm to the deep network structure,and then adds the convolutional neural net-work module as the sparse representation of the imaging area and its inverse process.The parameters that need to be manually adjusted are set to learnable network parameters.Finally,the network is trained and tested using the down-sampling echo data after clutter suppression.The simulation and measured data processing results show that this method can improve the imaging accuracy of underground targets without manual adjusting parameters.
deep unfolding networkstepped frequency ground penetrating radarfast iterative shrinkage thres-hold algorithmcompressive sensing
国家科技期刊平台
NSTL
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