Research On Compressed Sensing Sparse Imaging Algorithm For Ultra-wideband Through-wall Rada

Relying on the strong penetrating ability of low frequency electromagnetic waves,through-wall radar can not only observe hidden targets through walls and other obstacles,but also image the layout of buildings or targets behind walls,making it widely used in the military and civilian sectors.The development of ultra-wide-band technology improves the resolution of radar imaging,but high resolution also brings a lot of data.The theory of compressed sensing provides an idea for solving large-scale data acquisition,which points out that if the signal is sparse or can be sparsely represented,the signal could be reconstructed without distortion even at lower than Nyquist sampling frequency.Therefore,compressed sensing theory can be applied to the field of ultra-wide-band through-the-wall radar sparse imaging.This paper mainly focuses on the issue of imaging targets behind a single wall,with the following three components being implemented:(1)The ultra-wideband through-the-wall radar imaging system uses wideband signals and large-aperture array antennas,which generates a large amount of data.To solve this problem,a sparse imaging algorithm based on fractional smoothed L0 norm is proposed.Before imaging,the wall clutter is removed in advance by a wall clutter suppression algorithm.And then the imaging problem is transformed into the L0 norm minimization problem by exploiting the sparsity of the target behind the wall.In the process of image reconstruction,a fractional smoothing function is proposed to better approximate the L0 norm,and the nonlinear conjugate gradient method is used to solve the minimization problem.The simulation results show that the proposed algorithm can accurately image the target position under the condition of under-sampling,and has better anti-noise performance.(2)The echo signal of the target is always completely submerged by the wall clutter,which makes it impossible to reconstruct the exact position of the target.However,the wall clutter suppression and the behind-the-wall target imaging are often divided into two unrelated steps.To solve this problem,a through-the-wall imaging algorithm combined with low-rank,sparse and total variation is proposed in this paper.Firstly,the total echo signal is regarded as the sum of the wall echo signal with low-rank and the target echo signal with sparsity.Then,the wall clutter suppression problem and the sparse target imaging problem behind the wall are regarded as solving a low-rank and sparsity constrained optimization problem.Finally,a total variation constraint is added to maintain the profile information of the imaged target.Compared with the algorithm based on low-rank and sparse constraint,the imaging effect of the proposed algorithm is better,and the contour of the target can be clearly seen.(3)In order to facilitate the test work,a test software for through-the-wall radar sparse imaging algorithm based on Lab VIEW is developed.The system parameters such as the sampling rate required for the target imaging behind the wall can be set in the Lab VIEW software according to the requirements.At the same time,different sparse imaging algorithms are also provided for selection.After that,the imaging algorithm was called through MATLAB Script node.Finally,the 2D and 3D imaging results of the target and the performance index value of the imaging results can be observed in the front panel of Lab VIEW,so as to complete the test software development of the through-the-wall radar sparse imaging algorithm.