This paper addresses the problem of scene reconstruction in conjunction with wall-clutter mitigation for com- pressed multi-view through-the-wall radar imaging (TWRI). We consider the problem where the scene behind- the-wall is illuminated from different vantage points using a different set of frequencies at each antenna. First, a joint Bayesian sparse recovery model is employed to estimate the antenna signal coefficients simultaneously, by exploiting the sparsity and inter-signal correlations among antenna signals. Then, a subspace-projection technique is applied to suppress the signal coefficients related to the wall returns. Furthermore, a multi-task linear model is developed to relate the target coefficients to the image of the scene. The composite image is reconstructed using a joint Bayesian sparse framework, taking into account the inter-view dependencies. Experimental results are presented which demonstrate the effectiveness of the proposed approach for multi-view imaging of indoor scenes using a reduced set of measurements at each view.
In this paper, a distributed compressive sensing (CS) model is proposed to recover missing data samples along the
temporal frequency domain for through-the-wall radar imaging (TWRI). Existing CS-based approaches recover
the signal from each antenna independently, without considering the correlations among measurements. The
proposed approach, on the other hand, exploits the structure or correlation in the signals received across the array
aperture by using a hierarchical Bayesian model to learn a shared prior for the joint reconstruction of the high-resolution radar profiles. A backprojection method is then applied to form the radar image. Experimental results
on real TWRI data show that the proposed approach produces better radar images using fewer measurements
compared to existing CS-based TWRI methods.
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 CS-based 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.
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