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Variational Bayesian Compressive Multipolarization Indoor Radar Imaging

Journal Article


Abstract


  • This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced.

Publication Date


  • 2021

Citation


  • Tang Ha, V., Bouzerdoum, A., & Phung, S. L. (2021). Variational Bayesian Compressive Multipolarization Indoor Radar Imaging. IEEE Transactions on Geoscience and Remote Sensing. doi:10.1109/TGRS.2021.3051955

Scopus Eid


  • 2-s2.0-85100500421

Abstract


  • This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced.

Publication Date


  • 2021

Citation


  • Tang Ha, V., Bouzerdoum, A., & Phung, S. L. (2021). Variational Bayesian Compressive Multipolarization Indoor Radar Imaging. IEEE Transactions on Geoscience and Remote Sensing. doi:10.1109/TGRS.2021.3051955

Scopus Eid


  • 2-s2.0-85100500421