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

Multiview through-the-wall radar imaging (TWRI) can improve the imaging quality and target detection by exploiting the measurement data acquired from various views. Based on the established joint sparsity signal model for multiview TWRI, a cross validation (CV) based distributed greedy sparse recovery algorithm which combines the strengths of the CV technique and censored simultaneous orthogonal matching pursuit algorithm (CSOMP) is proposed in this paper. The developed imaging algorithm named by CV-CSOMP which separates the total measurements into reconstruction measurements and CV measurements is able to achieve the accurate imaging reconstruction and estimation of recovery error tolerance by the iterative CSOMP calculation. The proposed CV-CSOMP imaging algorithm not only can reduce the communication costs among radar units, but also can provide the desirable imaging performance without the prior information such as the sparsity or noise level. The experimental results have verified the validity and effectiveness of the proposed imaging algorithm.

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