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

This paper presents a new image formation method for multi-polarization through-the-wall radar imaging. The proposed method combines wall clutter mitigation and scene reconstruction in a unified framework using multitask Bayesian compressed sensing. First, the radar signals are jointly recovered using Bayesian compressed sensing in the wavelet domain. Then, a subspace projection method is employed to mitigate the front wall reflections. This is followed by principal component analysis, which is used to compress the remaining wavelet coefficients and remove noise. A linear model is developed which relates the compressed wavelet coefficients directly to the image of the scene. For scene reconstruction, multitask Bayesian compressed sensing is further applied to simultaneously form the images associated with all polarimetric channels. Experimental results based on real radar data demonstrate that the proposed method improves image quality by enhancing target reflections and attenuating background clutter.