Group Sparse Basis Pursuit Denoising Reconstruction Algorithm for Polarimetric Through-the-Wall Radar Imaging

Abstract

Polarimetric through-the-wall radar imaging (TWRI) system has the enhancing performance in the detection, imaging, and classification of concealed targets behind the wall. We propose a group sparse basis pursuit denoising-(BPDN-) based imaging approach for polarimetric TWRI system in this paper. The proposed imaging method combines the spectral projection gradient L1-norm (SPGL1) algorithm with the nonuniform fast Fourier transform (NUFFT) technique to implement the imaging reconstruction of observed scene. The experimental results have demonstrated that compared to the existing compressive sensing-(CS-) based imaging algorithms, the proposed NUFFT-based SPGL1 algorithm can significantly reduce the required computer memory and achieve the improved imaging reconstruction performance with the high computational efficiency. Output: Y = y 1 , y 2 , ⋯, y L for l = 1 L, do for g = 0 M 1 − 1, do Use the type-III NUFFT technique to calculate (16) and obtain the vector y m g ,l = y m g ,l 0 , y m g ,l 1 , ⋯, y m g ,l N 1 − 1 T end for Reshape y m g ,l into the vector y l = y T m 0 ,l , y T m 1 ,l , ⋯, y T m M 1 −1 ,l

Height estimation for automotive MIMO radar with group-sparse reconstruction

“…To study the benefit of coupling the reconstructions at different points of the trajectory via the group-sparsity constraint for the partition (15), we compare in the next section the performance with the alternative approach of solving the sparse reconstructions (14) individually at each distance to the obstacle with the group partition (12). We call this approach step-by-step ("SbyS") range fusion, versus the "GS" approach.…”