An SVD-based approach for mitigating wall reflections in through-the-wall radar imaging

Abstract

In this paper, we mitigate wall EM returns in through-the-wall radar imaging (TWRI) using singular value decomposition (SVD). To suppress wall reflections, the SVD is applied to the B-scan matrix of the received signals. The signal space is decomposed into three subspaces: the clutter subspace, the target subspace, and the noise subspace. Then, a set of normalized and smoothed eigen-components are combined to produce the target signal. Finally, delay-and-sum beamforming is applied to the reconstructed B-scan matrix to form the image. Experimental results demonstrate that the proposed method is effective in removing background, reducing clutter, and highlighting the targets.978-1-4244-8902-2/11/$26.00 ©2011 IEEE

Compressive sensing for through wall radar imaging of stationary scenes using arbitrary data measurements

“…We first describe the signal model for through-the-wall propagation in the presence of a homogeneous wall and then discuss the wall mitigation approaches presented in [9] and [8].…”

“…This is because the subtraction of consecutive imaging results would eliminate both target and clutter. For this type of scenes, techniques to remove the front wall EM returns without diminishing the target have been devised [6][7][8][9][10]. These approaches were originally introduced to work on all data observations [6][7][8][9], and were later shown to be equally effective under partial data observations, thereby permitting the application of CS for sparse scene reconstruction [10].…”

“…For this type of scenes, techniques to remove the front wall EM returns without diminishing the target have been devised [6][7][8][9][10]. These approaches were originally introduced to work on all data observations [6][7][8][9], and were later shown to be equally effective under partial data observations, thereby permitting the application of CS for sparse scene reconstruction [10]. More specifically, direct applications of wall clutter mitigation techniques, such as spatial filtering [9] and subspace projection [8], were shown to be effective in [10], provided that the same reduced set of frequencies or time samples were used at each antenna position.…”

“…Next section provides the TWRI signal model and reviews the wall mitigation approaches presented in [9] and [8]. Section 3, presents the proposed compressive sensing approach used to reconstruct the range profiles along with the modified OMP.…”

Radar imaging of stationary indoor targets using joint low-rank and sparsity constraints

“…Imaging indoor scenes, however, is challenging due to strong front-wall electromagnetic (EM) returns. The front-wall EM reflections typically dominate those from targets, rendering target detection difficult or even impossible [3]. Furthermore, multiple reflections within the front wall produce reverberations which obscure the radar returns from weak targets.…”