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Variational Bayesian Compressive Multipolarization Indoor Radar Imaging

Tang Ha, V. and Bouzerdoum, A. and Phung, S.L. (2021) Variational Bayesian Compressive Multipolarization Indoor Radar Imaging. IEEE Transactions on Geoscience and Remote Sensing. ISSN 1962892 (In Press)

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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. IEEE

Item Type: Article
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1109/TGRS.2021.3051955
Uncontrolled Keywords: Bayesian networks; Clutter (information theory); Image reconstruction; Inference engines; Probability distributions; Radar; Radar clutter; Radar measurement; Bayesian formulation; Joint distributions; Joint probability distributions; Low dimensional structure; Polarization images; Posterior distributions; Variational bayesian; Variational Bayesian inferences; Radar imaging
Additional Information: Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/8817

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