Compressive-Sensing-Based High-Resolution Polarimetric Through-the-Wall Radar Imaging Exploiting Target Characteristics

Abstract

Abstract-In this letter, we consider high-resolution throughthe-wall radar imaging (TWRI) using compressive sensing (CS) techniques that exploit the target and sensing characteristics. Many TWRI problems can be cast as inverse scattering involving few targets and, thus, benefit from CS and sparse reconstruction techniques. In particular, recognizing that most indoor targets are spatially extended, we exploit the clustering property of the sparse scene to achieve enhanced imaging capability. In addition, multiple polarization sensing modalities are used to obtain increased observation dimensionality within the group sparsity framework. The recently developed cluster multi-task Bayesian CS approach is modified to effectively solve the formulated group and clustered sparse problem. Experimental results are presented to demonstrate the superiority of the proposed approach.

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

“…Thus, the polarimetric TWRI reconstruction problem is typically very difficult because of the huge size of dictionary matrix and high computational burden imposed by the polarimetric image formation algorithm. To solve this problem, the proposed imaging algorithm adopts SPGL1 algorithm to solve the group sparse BPDN problem of (7). The foremost reason for choosing SPGL1 solver is that it can find the solution of group sparse BPDN problem by using functional inputs for dictionary matrix instead of the explicit enumeration of dictionary matrix.…”

“…In [6], the multitask Bayesian CS (MT-BCS) framework is employed to simultaneously reconstruct the images associated with all polarimetric channels and provide the enhanced image quality compared to the imaging results formed individually for each polarization channel. A modified clustered MT-BCS algorithm which combines the multipolarization sensing group sparsity and spatially clustered sparsity is proposed in [7] to achieve enhanced imaging capability for extended targets. In [8], a greedy algorithm called look-ahead hybrid matching pursuit (LAHMP) is proposed to provide composite multipolarization imaging result with higher quality.…”

Structured Bayesian compressive sensing exploiting spatial location dependence

“…(19), which involves M × M matrix inversion and generally requires O(M 3 ) operations. However, by exploiting the property of Toeplitz matrix inversion, the complexity can be reduced to O(M 2 ) [30], which is comparable to that of the other BCS methods [9,10,22,24].…”

“…In fact, various spatial characteristics can be exploited in practice. For example, in through-the-wall imaging and structural health monitoring, targets and flaws of interest often have extended occupancies that are clustered in the image domain [16][17][18][19]. In the timefrequency analysis, frequency modulated (FM) signals have a sparse and continuous signatures in the time-frequency domain [20].…”

FDTD Based Dictionary Matrix for Sparsity-Based Through-Wall Radar Imaging

“…CS states that it is possible to accurately recover an unknown sparse signal from a limited number of measurements with high probability by solving a convex optimization problem [5,6]. Making use of the sparsity of the scene, CS was widely applied in TWRI, providing an efficient way of image reconstruction using far fewer observations [7][8][9][10].…”