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

Yanpeng Sun,Tianhong Yang,Lele Qu,Shimiao An

doi:10.1155/2018/6027654

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.

Highlights

Through-the-wall radar imaging (TWRI) based on ultrawideband (UWB) technology has received the substantial attention in recent years
The aforementioned compressive sensing- (CS-)based imaging approaches have been successfully applied to polarimetric TWRI system, these imaging algorithms require that the dictionary matrix should be explicitly constructed before the imaging process and are quite computationally intensive with huge memory burden, which severely limits the practical applications of these algorithms to large-scale polarimetric TWRI scenarios
By exploiting the joint sparsity model, the polarimetric TWRI formation problem is formulated as the group sparse basis pursuit denoising (BPDN) problem, which is solved by the spectral projection gradient L1-norm (SPGL1) algorithm [10, 11]

Summary

Introduction

Through-the-wall radar imaging (TWRI) based on ultrawideband (UWB) technology has received the substantial attention in recent years. Several imaging algorithms based on compressive sensing (CS) technique have been developed for polarimetric TWRI system to reduce the polarimetric measurement data and enhance the imaging quality. By exploiting the joint sparsity model, the polarimetric TWRI formation problem is formulated as the group sparse basis pursuit denoising (BPDN) problem, which is solved by the spectral projection gradient L1-norm (SPGL1) algorithm [10, 11]. Experimental results have shown that the proposed NUFFT-based SPGL1 imaging algorithm can provide the enhanced imaging performance with the dramatic reduction of required computer memory and computational complexity.

Signal Model

Proposed Imaging Algorithm

Experimental Results

Conclusion