Two-stage through-the-wall radar image formation using compressive sensing

Abstract

Abstract. We introduce a robust image-formation approach for through-the-wall radar imaging (TWRI). The proposed approach consists of two stages involving compressive sensing (CS) followed by delay-and-sum (DS) beamforming. In the first stage, CS is used to reconstruct a complete set of measurements from a small subset collected with a reduced number of transceivers and frequencies. DS beamforming is then applied to form the image using the reconstructed measurements. To promote sparsity of the CS solution, an overcomplete Gabor dictionary is employed to sparsely represent the imaged scene. The new approach requires far fewer measurement samples than the conventional DS beamforming and CSbased TWRI methods to reconstruct a high-quality image of the scene. Experimental results based on simulated and real data demonstrate the effectiveness and robustness of the proposed two-stage image formation technique, especially when the measurement set is drastically reduced. 1 Introduction Through-the-wall radar imaging (TWRI) is an emerging technology with considerable research interest and important applications in surveillance and reconnaissance for both civilian and military missions. [1][2][3][4][5][6] To deliver high-resolution radar images in both range and crossrange, TWRI systems use wideband signals and large aperture arrays (physical or synthetic). This leads to prolonged data acquisition and high computational complexity because a large number of samples need to be processed. New approaches for TWRI are therefore needed to obtain high-quality images from fewer data samples at a faster speed. To this end, this paper proposes a new approach using compressive sensing (CS) for through-the-wall radar imaging. CS is used here to reconstruct a full measurement set, which is then employed for image formation using delay-and-sum (DS) beamforming.CS enables a sparse signal to be reconstructed using considerably fewer data samples than what is required by the Nyquist-Shannon theorem. [7][8][9] In through-the-wall radar

Multi-polarization through-the-wall radar imaging using joint Bayesian compressed sensing

“…Therefore, we apply principal component analysis technique to compress the measurement vector and the dictionary resulting in a compact linear model. Let C denote the covariance matrix of the new measurements given in (17). Using the eigendecomposition, we can write…”

“…Several techniques based on compressed sensing (CS) have been developed for TWRI [13]- [17]. The scene reconstruction is posed as an inverse problem where a linear model is employed which relates the compressive measurements from a single polarimetric channel directly to the imaged scene, which is assumed to be sparse.…”

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

“…Recently, compressed sensing (CS) has been employed for fast data acquisition and accurate signal reconstruction from reduced data samples [5,6]. In TWR imaging, most CS techniques [7][8][9][10][11] assume that the wall returns can be completely removed before applying CS, or a background scene is available for suppressing the wall reflections. Very recently, wall mitigation techniques were investigated in the CS context [12][13][14].…”

Sparsity-Aware Human Motion Indication

“…Recently, CS has been considered for radar imaging due to its ability to reconstruct a high-resolution image from a reduced set of measurements [2,5,17,18,23,24,31]. The scene reconstruction is posed as an inverse problem, whereby a spatial map of reflections is formed from radar measurements.…”